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Innovation indicators throughout the innovation process: An extensive literature analysis

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How to evaluate innovations, especially in the beginning of new product development, is a question constantly posed by academics, managers, and policymakers. One reason for this is that improved front-end decisions greatly affect company performance. To find the answers to this question, this review article analyzes scientific publications on innovation indicators published between 1980 and 2015. The objective of this article is to increase the understanding of the indicator landscape and to complement the various stages of the innovation process with relevant indicators. In doing so, this study categorizes the identified indicators into company-specific and contextual dimensions. Furthermore, this study analyzes the indicators in terms of their potential for ex-ante and ex-post evaluation and investigates the characteristics of relevant publications. The analysis finds that more process rather than product indicators exist in the literature. Current publications emphasize qualitative and indirect indicators but neglect indicators for the early stages of the innovation process. The review identifies 82 unique indicators to evaluate innovations including 26 indicators for the early stages. The results can help managers, researchers, and policymakers to better understand the innovation process and the indicator landscape. However, more concrete indicators are needed to improve front-end innovation decisions.
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Technovation
journal homepage: www.elsevier.com/locate/technovation
Innovation indicators throughout the innovation process: An extensive
literature analysis
Marisa Dziallas
a
, Knut Blind
a,b,
a
Technical University of Berlin, Chair of Innovation Economics, Marchstraße 23, 10587 Berlin, Germany
b
Fraunhofer Institute for Open Communication Systems FOKUS, Kaiserin-Augusta-Allee 31, 10589 Berlin, Germany
ARTICLE INFO
Keywords:
Innovation indicators
Innovation factors
Ex-ante and ex-post
Innovation process
New product development
Innovation evaluation
ABSTRACT
How to evaluate innovations, especially in the beginning of new product development, is a question constantly
posed by academics, managers, and policymakers. One reason for this is that improved front-end decisions
greatly aect company performance. To nd the answers to this question, this review article analyzes scientic
publications on innovation indicators published between 1980 and 2015. The objective of this article is to
increase the understanding of the indicator landscape and to complement the various stages of the innovation
process with relevant indicators. In doing so, this study categorizes the identied indicators into company-
specic and contextual dimensions. Furthermore, this study analyzes the indicators in terms of their potential for
ex-ante and ex-post evaluation and investigates the characteristics of relevant publications. The analysis nds
that more process rather than product indicators exist in the literature. Current publications emphasize quali-
tative and indirect indicators but neglect indicators for the early stages of the innovation process. The review
identies 82 unique indicators to evaluate innovations including 26 indicators for the early stages. The results
can help managers, researchers, and policymakers to better understand the innovation process and the indicator
landscape. However, more concrete indicators are needed to improve front-end innovation decisions.
1. Introduction and motivation
What concrete indicators can be used to evaluate ideas and concepts for
innovations before their market entry and after their commercialization,
especially during the early stages of the innovation process? This question
is repeatedly asked by policymakers, managers, and academic researchers
(e.g., Becheikh et al., 2006;Dewangan and Godse, 2014). The increasing
number of publications examining innovation indicators and success fac-
tors reects the demand for answers to this question (Becheikh et al., 2006;
Freeman and Soete, 2009;Evanschitzky et al., 2012). However, despite the
existing research, the indicator landscape still needs to be better under-
stood. Specically, the front-end of the innovation process requires further
clarication (Eling and Herstatt, 2017).
For companies, indicators are indispensable to manage and control the
plethora of innovative ideas and concepts that are submitted to them. The
dened selection criteria are equally important for an ecient resource
allocation and performance evaluation in each phase of the innovation
process (Evanschitzky et al., 2012; Dewangan and Godse, 2014). For pol-
icymaking practices, it is signicant to have accurate indicators to evaluate
clearly the proposals of dierent applicants for innovation projects and to
assess the progress of subsidized projects. Improving the evaluation process
of innovations can also help investors to fund new ventures.
Given the signicant need to improve the understanding of innovation
indicators with a focus on the front-end of the innovation process (OECD,
2005), the interest of this study lies in the indicators and factors behind the
innovation performance throughout the innovation process (cf. Birchall
et al., 2011;p.1819; cf. Klenner et al., 2013,p.915).
Becheikh et al. (2006) published a systematic literature review on
technological innovations in the manufacturing sector from 1993 and
2003. Based on this review, the present study examines the characteristics
of innovation indicators, innovation dimensions, and factors. It also com-
plements the various stages of the innovation process with relevant product
innovation indicators and process innovation indicators. This com-
plementation leads to a comprehensive overview of all existing ex-ante
indicators, and it becomes a starting point for further research. This over-
viewisbasedonanextensiveliteraturereviewofscienticpublicationson
the indicators for technological and non-technological innovations pub-
lished between 1980 and 2015. Therefore, this study covers an extended
timeframe and a broader spectrum of industries.
Regarding prior research, the existing (e.g., Montoya-Weiss and
Calantone, 1994;Evanschitzky et al., 2012;Storey et al., 2016) and the
relationship between innovation and performance of small and
https://doi.org/10.1016/j.technovation.2018.05.005
Received 19 February 2017; Received in revised form 8 May 2018; Accepted 30 May 2018
Corresponding author.
E-mail addresses: marisa.dziallas@campus.tu-berlin.de (M. Dziallas), knut.blind@tu-berlin.de (K. Blind).
Technovation xxx (xxxx) xxx–xxx
0166-4972/ © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).
Please cite this article as: Dziallas, M., Technovation (2018), https://doi.org/10.1016/j.technovation.2018.05.005
medium-sized enterprises national culture (e.g., Rosenbusch et al.,
2011). Another meta-analysis synthesizes the results on the relationship
between the rate of new product development and their antecedents,
which are categorized into strategy, project, process, and team (Chen
et al., 2010). By contrast, the present study considered a generally
broader scope for its research as well as an extended time horizon and a
broader type of study. In particular, this study included not only
quantitative studies that are synthesized by meta-analyses but also
qualitative studies to show the entire indicator landscape.
This analysis can help to advance knowledge on innovation selection
indicators by synthesizing the existing results. These results can better
channel future studies focusing on the prioritization of innovation projects.
The rest of this paper is organized as follows. First, the related
theoretical background and the method used to identify the relevant
literature are discussed. Second, the distinguishing characteristics of the
relevant publications are presented. Third, the characteristics of in-
novation indicators throughout the innovation process with a focus on
ex-ante and ex-post indicators are analyzed. Finally, the paper con-
solidates the ndings and ends with the main conclusion, implications,
and recommendations for researchers, managers, and policymakers.
2. Background literature on innovation indicators and business
relevance
The understanding and denitions of innovation presented in the ex-
isting scientic literature vary greatly from one another, and therefore their
use in this study warrants clarication. In this study, innovation is dened
as invention plus exploitation,which is based on Roberts (1998, p. 13)
and later used by Dewangan and Godse (2014, p. 536), among others. This
denition includes the implementation of a new or signicantly improved
product, process, or service (OECD, 2005) and the commercialization of
innovation (Dewangan and Godse, 2014). Therefore, the term innovation
applies to a successfully commercialized new idea. For simplicity, this study
denes innovation as a term referring to both innovative ideas that are
intended to be commercialized in the market and ideas that have already
been successfully commercialized.
An indicator is considered a measured value that provides in-
formation about a specic phenomenon or a status quo. Information
can be given in an aggregated form, which facilitates a focused eva-
luation (Born, 1997). Borrás and Edquist (2013) considered innovation
indicators as the source of information from which one can detect
problems in the innovation system. This study dierentiates among the
terms indicator,factor, and dimension. The dimension is understood as
the broad eld to which the indicator relates (cf. Becheikh et al., 2006).
Factor is the more speciceld into which the indicator can be cate-
gorized. For example, a success factor of the market dimension is cus-
tomer satisfaction, and an indicator is the number of customer com-
plaints (Fraunhofer-Institut, 2007).
Regarding the stages of the innovation process, ex-ante refers to the
front-end of the innovation process. The front-end signies the generation,
screening, and evaluation of ideas and concepts for innovations (Khurana
and Rosenthal, 1998;Reid and de Brentani, 2004). Specically, the front-
end is the phase of the rst idea until the ideas enter the formal develop-
ment process, that is, the godecision to start the developing process and
to commit resources (Eling et al., 2016; Van Oorschot et al., 2018). By
contrast, ex-post refers to innovations that have already been introduced
into the market, that is, after the market launch.
As noted in the literature on indicators, a number of reviews, aside
from the mentioned meta-analyses, present innovation indicators and
factors. Nevertheless, the published reviews are insucient to under-
stand the characteristics of the entire innovation indicator landscape.
Thus far, precise selection indicators have not been investigated in
adequate detail (Cooper, 1999; Astebro, 2003; Bloch and Bugge, 2013).
In particular, ex-ante indicators that can be used in the early stages of
the innovation process have been neglected. Instead, researchers have
emphasized the inuencing factors of innovations (e.g., Balachandra
and Friar, 1997;Fleuren et al., 2014) or indicators that only partially
indicate innovations, such as patents (Kleinknecht et al., 2002).
In addition to literature reviews in the indicator research eld, prior
research also focused on innovation indicators from specic perspec-
tives (e.g., Cooper and Kleinschmidt, 1995;Kerssens-van Drongelen and
Cooke, 1997;Verhaeghe and Kr, 2002;Adams et al., 2006;Chiesa and
Frattini, 2009;Cruz-Cázares et al., 2013). The concerned literature fo-
cuses on the indirect and direct indicators (Becheikh et al., 2006). Ex-
amples of indicators that indirectly and partially evaluate innovations
are patents (Hagedoorn and Cloodt, 2003) and research and develop-
ment (R&D) budget (Flor and Oltra, 2004). Other indicators, such as the
number of new product ideas (Cooper and Kleinschmidt, 1993) and the
percentage of ideas with a commercialization potential (Dewangan and
Godse, 2014), directly evaluate innovations.
From a broader perspective, dierent understandings of indicators
can be found in the literature. For example, Patel and Pavitt (1995) as
well as Grupp and Schubert (2010) suggested using composite in-
dicators to measure innovation, as there is no catch-allindicator.
Other researchers focused on science, technology, and innovation in-
dicators (Freeman and Soete, 2009), and others emphasized input,
throughput, and output indicators (e.g., Klomp and Leeuwen, 2001).
Regarding policymaking, a well-known innovation survey using input-
and output-oriented indicators is the Community Innovation Survey (CIS)
of the European Union (Eurostat) executed by national institutions based
on the Commission Implementing Regulation (EU) No. 995/2012 of
October 26, 2012 (OECD, 2005; Eurostat, 2015). This questionnaire-based
method discusses the technical features and the economic signicance of a
companys innovative product (e.g., Kleinknecht and Bain, 1993;Cricelli
et al., 2016). However, many institutions face the problem of lacking in-
novation data. Companies are assumed to be unwilling to answer sensitive
questions about their innovation processes (Hansen, 1985; Chesnais, 1992).
The most well-known manual of international innovation indicators was
established by the OECDsOslo Manual 2005,which contains guidelines
for gathering and using information about industry innovation activities. A
prominent example of innovation measurement is the European Innovation
Scoreboard (EIS). The indicators are based on the CIS to compare the in-
novation performance of EU countries and those of Turkey, Iceland,
Norway,Switzerland,theUnitedStates,andJapan.TheEISfocuseson
national and regional comparisons (Hoelscher and Schubert, 2015).
In innovation evaluation in practice, the importance of measuring in-
novations is increasingly gaining the attention of managers and con-
sultancies. Examples of consulting surveys on innovation measures are the
one conducted by The Boston Consulting Group (Andrew et al., 2008,
2010),theMcKinseyinnovationmetricsurvey(Chan et al., 2008), and the
performance management survey by the Business Application Research
Center (Bange et al., 2009). Existing surveys demonstrate that rethinking a
businesss innovation measurement system is crucial (Dewangan and Godse,
2014); this nding is emphasized by practitioners as well. According to the
Boston Consulting Groups survey, 74% of managers believed that innova-
tion tracking should be included in central business processes, but only 43%
of companies actually measured innovations. Furthermore, 59% of the
companies noted that their innovation performance measurement system
was not eective (Dewangan and Godse, 2014).
Academic research does not indicate a common overall innovation
measurement framework. Moreover, whether the metrics from aca-
demic ndings are applicable to organizations remains unclear. For
example, Adams et al. (2006) claimed that the innovation measurement
methods recommended in the research literature seem to be too theo-
retical. These theoretical indicators are not straightforwardly applicable
to businesses (e.g., Adams et al., 2006;Cruz-Cázares et al., 2013). Even
a common understanding of the innovation process is missing, as it is
quite complex and includes diverse inuencing factors (Dodgson and
Hinze, 2000; Becheikh et al., 2006). In addition, a measurement
strategy to evaluate innovations is lacking (Edison et al., 2013). Con-
sequently, companies face the problem of measuring too few or insig-
nicant data, or they refrain from conducting any innovation
M. Dziallas, K. Blind Technovation xxx (xxxx) xxx–xxx
2
measurement at all (Andrew et al., 2008). Furthermore, organizations
disagree on what should be measured.
In sum, measuring newly evolving ideas is a considerable challenge. At
the very least, how and what to measure remain unclear when pre-devel-
opment projects could change in unexpected and diverse ways (Kirchho
et al., 2013). Another reason for the diculty in evaluating new ideas may
be the unavailable innovation data and methods (Andrew et al., 2008;
Edison et al., 2013). The use of indicators is a potential solution for this
evaluation problem because it unies innovation decisions.
Despite innovation indicators having been analyzed in the scientic
literature, additional indicators are needed to evaluate the commercial
potential of innovations throughout the innovation process. Specically,
ex-ante indicators that can be applied in the early stages of the innovation
process are required. To summarize, scholars of applied and theoretical
science as well as business practitioners emphasize the importance of in-
novation measurement in academia and businesses along with the need for
a better understanding of the innovation process and the indicator land-
scape (e.g., Birchall et al., 2011;Edison et al., 2013).
This study has an explorative character. Its main research objective is to
increase the understanding of the innovation indicator landscape and to
complement the various stages of the innovation process with relevant
indicators. Building on existing research, this study focuses on the fol-
lowing classications of dimensions and indicators that are expected to be
signicant for a better understanding of the relevant innovation indicators.
1. The identied indicators are categorized into company-specic and
contextual dimensions (Becheikh et al., 2006). The specic dimen-
sions are innovation culture, strategy, organizational structure, R&D
input and activities, competence and knowledge, nancial perfor-
mance and environment, market, and network.
2. In the innovation indicator literature, the indicators related to the
innovative products are published as e.g. the percentage of ideas
found viable for commercialization(e.g., Dewangan and Godse,
2014).
3. Companies use dierent criteria at dierent stages of the innovation
process. For example, time to marketis an indicator for evaluating the
length of time it takes from developing a product until the nal product
launch (e.g., De Felice and Petrillo, 2013).
4. The early stages of the innovation process require dierent in-
dicators in comparison with the later stages (Hart et al., 2003). In
the literature and in practice, the understanding of the front-end of
the innovation process is more supercial than that of the later
stages (Cooper, 2008;Barczak et al., 2009). However, front-end
indicators are signicant for the organizational and strategic deci-
sion-making process. They also support resource and activity de-
ployment (Hauser and Zettelmeyer, 1997; Hart et al., 2003).
Therefore, the current study categorizes product and process in-
dicators into ex-ante and ex-post criteria.
5. Indicators have dierent characteristics. Hardindicators are
quantitative in nature, whereas softindicators are qualitative in
nature (cf. Freudenberg, 2003, p. 9). Following this dierentiation,
the present study categorizes the identied indicators into hard and
soft criteria.
6. Relevant scientic studies presented indicators that indirectly and
directly measure innovations (Becheikh et al., 2006). The current
study expands their grouping and identies the indicators that di-
rectly or indirectly inuence the success of an innovation. Success is
considered as the successful commercialization of innovations in
terms of high sales rates (Astebro and Michaela, 2005). A direct
indicator is the percentage of ideas found viable for commerciali-
zation(Dewangan and Godse, 2014)orfuture duration of pro-
duct(Astebro and Michaela, 2005). Examples of indirect indicators
are newness to business(Duhamel and Santi, 2012), and plan-
ning and monitoring of the innovation process(Huergo, 2006). The
evaluation used in this study is based on the knowledge of experts
working in the innovation management eld. Therefore, this study
merely provides an initial categorization that requires further in-
vestigation.
7. To display the entire innovation indicator landscape, this study
presents the interplay of soft and hard indicators with direct and
indirect indicators.
3. Methodology
The study analysis consists of four steps. These steps are explained
in further detail in the following subsections.
3.1. Description of the analysis process
The rst step is a keyword search in three main databases to gather
relevant literature sources on innovation indicators. The databases used
are Science Direct, Web of Science, and Scopus. As part of this step,
papers from experts working in the innovation management eld are
also considered. In the second step, the indicators are synthesized to a
higher level of dimensions based on the model of Becheikh et al. (2006),
as shown in Fig. 1, to categorize the relevant indicators. The dimensions
are adapted to the results that have been identied in the referenced
literature. Indicators and factors are aligned with the internal and
contextual dimensions (Becheikh et al., 2006).
Additionally, each indicator is categorized into hardor soft,
reecting the quantitative or qualitative aspects, respectively (cf.
Freudenberg, 2003, p. 9). In the third part of the procedure, each in-
dicator and each factor are evaluated for whether they directly or in-
directly inuence the success of an innovation. In this part, success is
dened as the enhanced new product performance or the higher success
rate of products. An example of a direct indicator is the number of sales
of new products, which primarily measures innovation success (e.g.,
Grin and Page, 1993). An indirect indicator, such as patents, secon-
darily inuences innovation success (e.g., Acs and Audretsch, 1989).
In the fourth step, the indicators are assigned to a phase of the in-
novation process (cf. Cooper, 2008). This assigning procedure is important
to reect the entire innovation process and to control for the ex-ante ap-
plicability of each indicator. The following stages of the innovation process
are based on Cooper (1990) and Hart et al. (2003):strategy, product de-
nition, product concept, validation phase, production, and market launch. Steps
threeandfourofthisreviewareconductedbyanindependenttwo-stage
evaluation process by experts working in the innovation management eld.
In some cases, no consensus is found on the classication of indicators. The
results are then discussed to arrive at an agreement.
3.2. Article selection process and identication of innovation indicators
The literature review comprises 226 articles, which were selected by
a structured keyword search in the above-mentioned databases cov-
ering the timeframe of 19802015. The following keywords were used
Fig. 1. Adapted framework of Becheikh et al. (2006) to categorize the in-
dicators.
M. Dziallas, K. Blind Technovation xxx (xxxx) xxx–xxx
3
to conduct the literature search: innovation indicators, innovation AND
indicator, innovation measure or measurement, innovation perfor-
mance measurement, key or success factors AND innovation, process
indicator AND innovation, product indicator AND innovation, de-
terminants of innovation, and factors of innovation.
The period of 35 years was chosen because it catches a wide range of
articles and helps to present the evolution of the innovation indicator lit-
erature. To proceed systematically, seven inclusion criteria were specied
(Alderson et al., 2004).
The material collection comprises only peer-to-peer reviewed arti-
cles in scholarly journals to obtain a comparable body of research. To
include scientic literature from several research elds and current
developments, the search includes a wider area of journals and is not
limited to a certain cluster of journals. The inclusion criteria for the
innovation indicator literature are as follows:
1. Available in at least one of the three mentioned databases or cited in
one of the relevant articles;
2. Comprises one of the keywords of indicator,”“factor,or de-
terminantof innovation in the title, abstract or full text;
3. Journal publications;
4. Peer-reviewed articles;
5. Published between 1980 and 2015;
6. Articles in English;
7. Articles considering product or process innovations.
The studies were selected as described in Fig. 2. By using the re-
search criteria, 1796 potential articles were identied. Some full texts
of relevant articles were not accessible through the mentioned data-
bases. In this case, the authors who published in the journals included
in the Social Science Citation Index (SSCI), were asked for their articles
directly. Based on the title and abstract criteria, 1270 articles were
excluded. The full texts of the remaining 526 potentially relevant ar-
ticles were screened in more detail. Among the articles, 300 did not
meet the inclusion criteria and were thus excluded. In total, 226 articles
matched all the inclusion criteria. Either one (or more) of the innova-
tion dimensions or indicators and the factors were presented in the
identied study to generate a transparent process. Thus, the review
process is replicable and scientic. Consequently, more reliable results
were generated from which conclusions could be drawn (Cook et al.,
1997). As a basis for further analysis, a Microsoft Excel database was
generated and included the following indicator-related information:
identied indicators, factors and dimensions, innovation process phase, di-
rect and indirect inuence on innovation success, quantitative and qualita-
tive nature as well as publication year, journal, country of authors institu-
tion, country of investigation, type of industry, statistical method used for
data analysis, qualitative methods to study innovation, and the h5-index of
the relevant journal to test for quality of journals.
4. Results
This section presents and discusses the ndings from the literature
review on innovation indicators. The analysis results based on the re-
levant publications are rst shown, followed by the results of the in-
dicator analysis.
4.1. Findings from the publication analysis
4.1.1. Development of innovation indicator publications
Fig. 3 shows the development of publications per year. As shown
above, the number of publications between 1980 and 1995 is very low.
For 7 of the 16 years, no publication is indicated by the databases. Only
in 1993 are there more than six articles published addressing the in-
novation indicator topic. Beginning at zero or with a low number of
articles, the number of publications increases slightly in 1996. Notably,
from 2012 onwards, the research focus on innovation indicators in-
creases signicantly, with a peak in 2015 of 20 publications. In general,
the trend describes an increase in publications over the years from 1980
to 2015. The increase may be due to the series of national innovation
surveys that contribute to the European CIS in 1993, 1997, 2001, 2005,
2007, 2009, 2011, 2013, and 2015 (e.g., OECD, 2005;Eurostat, 2015;
Behrens et al., 2017).
Table 1 shows the number of publications per journals. Most articles
have been published in Research Policy (RP), followed by Technovation,
Procedia-Social and Behavioral Sciences (PSBS), and Journal of Product
Innovation Management. This nding is in accordance with the high
impact factors of these leading journals (Journal Citation Reports 2017,
Clarivate Analytics, 2018). That is, RP ranks 11th among the world's top
journals in Management and rst in the Planning & Development ca-
tegory according to the Social Sciences Citation Index (Elsevier, 2018a,
referring to Social Sciences Citation Index®by ©Thomson Reuters
Journal Citation Reports, 2008).
The impact factor of RP is 3.470 (Elsevier, 2018a). Its higher impact
factor compared with other journals implies a high scientic quality of
the relevant articles included in the literature review. The impact factor
of Technovation is 2.243 (Clarivate Analytics, 2018; Elsevier, 2018b).
These top journals are followed by other leading journals, such as the
R&D Management,Scientometrics, and Expert Systems with Applications or
Economics Letters. The high rank of journals is an indication of the high
quality of the renowned publications used in this study.
In the h5-index, RP ranks 8th, and Technovation ranks 27th. PSBS is not
listed under the top journal rankings based on the h5-index (h5-index
40). The h5-index is used for articles published within the last ve years.
The index means that h publications have been cited at least ve times
(each) within the last ve years. The list of journals used in the analysis
with the available h5-index can be found in the Appendix (Table 10).
4.1.2. Innovation indicator publications at the country level
Table 2 shows the number of publications per country that is in-
vestigated in the corresponding study (second column) and per country of
the authorsaliated university (third column). If a continent was in-
vestigated or if a worldwide investigation has been conducted, the study
was excluded because identifying one speciccountrywouldbeim-
possible.
The United States, Germany, the United Kingdom, and Spain are the
leading countries in both the investigated country and the country of
the authorsaliation. However, the UK, Spain, and Germany inter-
change in the second, third and fourth places, depending on the view of
the country (country of investigation or country of the authors af-
liated university). The majority of these authors investigated their
country of aliation and infrequently studied an additional country
Fig. 2. Flow diagram of the literature review (19802015).
Fig. 3. Publication numbers per year (19802015).
M. Dziallas, K. Blind Technovation xxx (xxxx) xxx–xxx
4
(e.g., Baptista and Swann, 1998;Mattes et al., 2006;Alegre and Chiva,
2008;Lecerf, 2012;Pekovic et al., 2015). Another reason for the high
number of publications in the United States and the United Kingdom
may be that several high-ranking universities are located in these
countries, such as the London Business School and University of Oxford
in the United Kingdom and Harvard University, Stanford University,
and Massachusetts Institute of Technology in the United States (e.g.,
Top universities, 2015). Although China is only in the 8th or 10th place,
the publications investigating data on Chinese industries have been
increasing since 2009. Canada, the Netherlands, and Turkey are coun-
tries that are usually investigated in comparison with India, Portugal, or
South Korea, for example.
In terms of the countries and the investigated industry, research
interest in the United States focuses heavily on patents (Acs and
Audretsch, 1989; Dror, 1989; Lanjouw and Schankerman, 2004; Mattes
et al., 2006; Belenzona and Patacconi, 2013). In the United Kingdom,
interest lies in the qualication and experience of employees (Homan
et al., 1998; Romijn and Albaladejo, 2002) and in the interactions with
external partners, such as universities, suppliers, or customers (Romijn
and Albaladejo, 2002), among others. Research interest in Germany is
varied, as authors focus on external inuences on innovation (Brem and
Voigt, 2009), project delay (Feurer et al., 1996), patent stocks
(Czarnitzki and Kraft, 2004), and other areas. Regarding industry,
German authors analyze the branches of manufacturing (Czarnitzki and
Kraft, 2004) and technology-based services (Brem and Voigt, 2009).
Spanish authors show particular interest in the tourism and agriculture
sectors, such as food and beverage (March-Chordà et al., 2002; Nieves
et al., 2014), as well as in ceramic tile industry (Flor and Oltra, 2004)
and start-up branch. These interests are an indication of innovation and
creative activity (Sáez-Martínez et al., 2014). Compared with other
sectors, the manufacturing sector is generally presented more often
(Huergo, 2006; Vega-Jurado et al., 2008; Gonzalez-Benito et al., 2015).
For example, Turkey has shown a growing interest in the innovation
indicator research since 2001. In this country, industries such as yacht
building and software development (Koc, 2007) as well as small com-
panies (e.g., Bayarçelik et al., 2014) have been investigated.
4.1.3. Innovation indicator publications at the industry level
The following chapter analyzes the industries that have been in-
vestigated in the used literature. The analysis is based on the NACE
(Nomenclature statistique des activités économiques dans la Communauté
européenne) classication (Eurostat, 2008). The manufacturing industry,
which has been studied the most, has a score of 74%. The huge amount of
research on innovation indicators in the manufacturing industry underlines
the interest in the evaluation criteria for innovations at the company level.
The remaining industries account for 14% of the relevant publications.
These other studied industries are usually situated in the service sector. The
data show a shift from the manufacturing to the service industry. This shift
couldhaveresultedfromspeci
ctrends, such as digitization, big data
movement, and the need for companies to focus on services for customers to
be successful in the market (Hipp and Grupp, 2005; Chandler, 2015).
However, the number remains quite low when compared with that of the
manufacturing industry. Publications related to diverse industriesand
articles that did not focus on a specic branch are excluded in the analysis
because the corresponding industries arenotwelldescribedinthesearticles.
4.1.4. Methods used to study innovation
Fig. 4 indicates that, neglecting other and unspecied methods,
regression analysis is applied the most frequently at 27% (53 times) in
comparison with the other methods of relevant publications.
Table 1
Number of publications per journal (19802015).
Journal Number of publications per
journals
Research Policy 36
Technovation 17
Procedia - Social and Behavioral Sciences 11
Journal of Product Innovation Management 9
R&D Management 8
Scientometrics 6
Creativity and Innovation Management 3
Economics Letters 3
Expert Systems with Applications 3
Journal of Business Venturing 3
Research Evaluation 3
Research Technology Management 3
Review of Industrial Organization 3
Technology Analysis & Strategic
Management
3
Academy of Management Journal 2
Applied Economics 2
IEEE Transaction on Engineering
Management
2
Industrial Marketing Management 2
Industry and Innovation 2
International Journal of Innovation
Management
2
International Journal of Technology
Management
2
Journal of Cleaner Production 2
Journal of Communication 2
Journal of Engineering and Technology 2
Journal of Marketing Research 2
Management Science 2
Small Business Economics 2
Strategic Management Journal 2
Structural Change and Economic Dynamics 2
Technological Forecasting & Social Change 2
The Economic Journal 2
World Patent Information 2
Note: This table shows only publications that occurred more than two times
between 1980 and 2015.
Table 2
Number of publications per country where the survey was conducted and per
country of the authorsaliated university (19802015).
Country Number of publications per
country where the survey
was conducted
Number of publications per
country of the authors
aliated university
USA 26 37
Spain 15 19
Germany 14 22
United Kingdom 11 22
Canada 10 13
Turkey 10 12
Netherlands 8 17
France 8 4
Italy 7 9
China 5 9
Finland 4 5
Japan 4 3
Switzerland 4 5
Australia 3 6
Belgium 3 8
Denmark 3 0
Greece 3 0
Sweden 3 5
Brazil 2 4
Columbia 2 2
Croatia 2 0
Ireland 2 0
Malaysia 2 2
Norway 2 4
Poland 2 0
Portugal 2 2
Slovenia 2 2
South Korea 2 2
Taiwan 2 5
Thailand 2 0
India 0 3
Hungary 0 2
Israel 0 2
Singapore 0 2
M. Dziallas, K. Blind Technovation xxx (xxxx) xxx–xxx
5
Regression analysisincludes all types of regression analyses, which
are not listed here individually. The other data analysis techniques to
investigate innovation are descriptive analysis (21%) and correlation
analysis (14%). The presented methods show that factor analysis
(10%), ordinary least squares regression (8%), structural equation
model (6.5%), and probit model (6%) are the frequently used methods.
To sum up, the relevant articles discuss dierent methods to study in-
novations. Most of these identied methods are used individually or
jointly. However, to present the methodological landscape, each
method is pointed out separately in this study. Articles that did not use
a common quantitative method are not listed. Conceptual models are
rarely used (only two times) (Cooper and Kleinschmidt, 1993; Brown
and Eisenhardt, 1995).
4.2. Synthesis of the literature: company-specic and contextual indicators
and factors
To classify the broad range of indicators, a framework is set up
based on the model of Becheikh et al. (2006), as previously mentioned.
The framework was adapted and rened for this article, as shown in
Fig. 1, and it is complemented by the numbers shown in Fig. 5.
After screening the relevant literature, the indicators are categor-
ized into company-specic and contextual dimensions. These dimen-
sions determine the innovation process and the resultant innovation
product. The identied indicators are classied according to these di-
mensions. As previously mentioned, an example of an innovation factor
of the market dimension is customer satisfaction, and an indicator is the
number of customer complaints (Fraunhofer-Institut, 2007).
Multiple factors related to internal and external elements aect the
ability of companies to implement innovations successfully (Rothwell
et al., 1974). Therefore, two categories are set up. First, company-
specic dimensions include those that are particular to a company, such
as culture or structure, and that aect the organizational innovation
behavior (e.g., Souitaris, 2002a). Second, contextual dimensions are
related to a company and its surrounding environment (Becheikh et al.,
2006). The latter dimension is based on the contingency approach
(Lawrence and Lorsch, 1967; Woodward, 1970), which denes a
company as an adaptive system that reacts to the surrounding en-
vironment in terms of its strategy, structure, and culture (Becheikh
et al., 2006). In total, 11 dimensions are determined (without innova-
tion project management). The dimensions, their attached factors and
indicators, and their inuence on innovations and the innovation pro-
cess are explained in detail in the next section. Nonetheless, the focus
lies on the product and process indicators, which are also presented in
the subsequent section. The study takes a broad view on the indicators,
followed by a more focused view on the product and process indicators.
Only a few publications examine the precise indicators. Many au-
thors analyze dimensions or factors that inuence innovations instead
of using actual indicators. Out of the 800 relevant dimensions and in-
dicators found in the literature review, only 371 indicators are men-
tioned in the relevant publications. This number represents the number
of scientic publications that investigates indicators. Regarding this
calculation, the dimensions and indicators are counted twice (or more).
As for the unique indicators, 82 product and process indicators and
factors to evaluate innovations throughout the innovation process are
identied in the relevant literature.
4.2.1. Company-specic dimensions: Culture, strategy, structure, and R&D
activities
Table 3 shows examples of the factors and indicators that are ca-
tegorized under the company-specic dimensions identied in the in-
novation literature between 1980 and 2015. The full list of categories
and indicators of the company-specic dimensions is found in the
Appendix (Table 11).
The following subsection explains the company-specic dimensions
in detail.
4.2.1.1. Strategy and vision. A companys strategy denes the future
activity elds to achieve long-term company goals, and it is the baseline
for dening a companys organizational innovation goals (Porter, 1987;
cf. Souitaris, 2002a;Astebro and Michaela, 2005). At only 4%, the
strategy dimension is presented the least (together with the network
dimension). A category of this dimension is innovation strategy (Adams
et al., 2006; Kamasak, 2015). The number of newly created innovative
opportunities is a strategy indicator (Hittmar et al., 2015).
4.2.1.2. Innovation culture. Integrating innovation into the company
culture is an important means to achieve success and to foster
innovation capabilities (Bullinger et al., 2007). The beliefs and values
of a company inuence the risk tolerance, personal development, and
innovation activities of employees and their motivation to develop and
implement new ideas (Menzel et al., 2007). The innovation culture
(Fig. 5) is mentioned comparatively often. It constitutes 20% along with
organizational structure. The indicators to measure the innovation
culture are the percentage of leaders trained in creativity techniques
(Chiesa et al., 1996) and the amount of time managers spent with the
management of an innovation compared with their usual tasks (Hittmar
et al., 2015), among others.
4.2.1.3. Competence and knowledge. The competences and knowledge
of a companys employees are crucial resources for new ideas and
innovation projects. The individual competence is the ability to
implement knowledge into actions to achieve the dened goals. At
9%, this dimension (Fig. 5) is presented quite often. Innovation-
oriented learning (De Medeiros et al., 2014) is a category in this eld.
4.2.1.4. Organizational structure. The organizational structure regulates
how rules, hierarchies, and responsibilities are established, controlled,
and coordinated. Regarding the previously identied publications, the
relation between business size and innovation is investigated
comparatively often in the reviewed literature. However, dierent
results have been published. On one hand, small companies seem to
have an advantage in the management of their innovations (Rothwell,
Fig. 4. Statistical and econometric methods used to study innovation
(19802015).
Fig. 5. Dimension framework for synthesizing the innovation indicators based
on the dimension framework of Becheikh et al. (2006).
M. Dziallas, K. Blind Technovation xxx (xxxx) xxx–xxx
6
1986; Bughin and Jacques, 1994). On the other hand, large companies
are more likely to invest in innovative projects because they can
allocate greater R&D resources than small rms (Becheikh et al.,
2006). Furthermore, studies found that size determines the
relationship between the management of innovations and the
marketing of innovations (Gonzalez-Benito et al., 2015). In addition
to the lack of capital, small companies partly face the challenge of
information decits, such as missing details about innovation policy
instruments, technical information, and highly qualied employees
(Kleinknecht, 1989). One reason for the diverging results in the
analyzed literature may be that these publications investigated the
relationship between business size and innovation in dierent contexts
(i.e., dierent countries, periods, and methods). Nevertheless, this
dimension has been investigated frequently, with a factor of 10%. An
indicator for this dimension is business size (Huergo, 2006), and team
satisfaction is considered a success factor (Grin and Page, 1993).
4.2.1.5. R&D activities and input. R&D activities and input are related to
the nancial situation of a business (Beneito, 2003) and the availability of
resources. Resources in this sense refer to employees, technology, tangible
assets (e.g., machinery, tools, and materials), time spent with an
innovation, and investments made to develop and realize innovative
products. Dierent results are found in the relevant publications. For
example, R&D personnel ratio (internal expertise) has a strong positive
eect on product and process innovation, and process innovation is also
inuenced by R&D intensity (R&D investment) (Song and Oh, 2015). R&D
intensity seems to be inuenced by knowledge and technology transfer
activities (Arvanitis et al., 2008). Arvanitis et al. (2008) dened the
innovation performance of companies as the R&D intensity and the
number of sales of new products. In the examined literature, R&D budget
(Flor and Oltra, 2004), or business investment (Sosnowski, 2014), is listed
as one of the main indicators to measure innovative activity and recognize
innovating corporations. This indicator might be considered to be used to
Table 3
Examples of categories and indicators categorized under the company-specic dimensions (1980-2015).
Factor Indicator Relevant references
Innovation culture Innovation culture of organization Bayarçelik et al., 2014;Slater et al., 2014;Naranjo-
Valencia et al., 2015
Creativity Percentage of leaders trained in
creativity techniques,
atmosphere
Chiesa et al., 1996;Ayob et al., 2012;Edison et al., 2013;
Yang et al., 2015
Companys entrepreneurial orientation/spirit Al-Mubaraki et al., 2015;Gonzalez-Benito et al., 2015
Top management support Amount of time managers spent
with innovations compared to
normal tasks
March-Chordà et al., 2002;Graner and Mißler-Behr,
2013;Hittmar et al., 2015
Openness of company towards change and innovation Number of external ideas/
generated with customers
Enkel et al., 2005;Ogawa and Piller, 2006;Lene, 2008;
Dewangan and Godse, 2014
Resistance to change Veugelers and Cassiman, 1999
Strategy Innovation strategy Adams et al., 2006;Kamasak, 2015
New product strategy Huang et al., 2004
Strategic t of innovation Number of newly created
innovative opportunities
Grin and Page, 1993;Hittmar et al., 2015
Willingness to take risks Aiman-Smith et al., 2005;Astebro and Michaela, 2005;
Wan et al., 2005;Salomo et al., 2007;Escalfoni et al.,
2011;Murro, 2013
Knowledge and
competence
Innovation-oriented learning Number of managers trained in
the methods and tools of
innovation
Kerssens-van Drongelen and Cooke, 1997;Banerjee,
1998;Astebro and Michaela, 2005;De Medeiros et al.,
2014;Hittmar et al., 2015
Openness towards knowledge Caloghirou et al., 2004
Internal knowledge resources, experiences and
background of founder/managers
Use of internal and external
knowledge and information
sources
Caloghirou et al., 2004;Sawang, 2011;Kamasak, 2015;
Kato et al., 2015
Organizational structure Business data, organizational factors Size of the company,
Geographic location of the
company
Wan et al., 2005;Huergo, 2006;Krasniqi and Kutllovci,
2008;Koouba et al., 2010;Tohidi and Jabbari, 2012;
Wang, 2012;Frey et al., 2013;Slater et al., 2014;De
Fuentes et al., 2015;Kamasak, 2015;Pekovic et al., 2015Age of company
External and internal growth
Formal structure
Flexibility, rapid adaptation to customers Wu et al., 2002;Krasniqi and Kutllovci, 2008;
Suwannaporn and Speece, 2010
Internal communication Lester, 1998;Suwannaporn and Speece, 2010
Good team structure together with appropriate
leadership
Accountable, dedicated,
supported cross-functional
teams with strong leaders
Cooper and Kleinschmidt, 1993;Grin and Page, 1993;
Cooper, 1999;Hollemann et al., 2009;Weiss et al., 2011
Team satisfaction
Research and development
activities and input
Willingness to invest in innovation/R&D, willingness
to conduct new research projects = sucient amount
of investment, nancial resources dedicated to
innovation
Research activities
R&D expenditure/investment Avermaete et al., 2004;Caloghirou et al., 2004;Katz,
2006;Chiesa et al., 2009;Belitz et al., 2011;Weiss et al.,
2011;Tohidi and Jabbari, 2012;De Felice and Petrillo,
2013;Edison et al., 2013;Makkonen and van der Have,
2013;Dewangan and Godse, 2014;De Medeiros et al.,
2014;Kim, 2014;Cavdar and Aydin, 2015;De Fuentes
et al., 2015
Average expenditure per
selected idea
Percentage of sales related to
new projects
Share of research budget from
total company budget
Innovation expenditure
Share of technology transfer
Financial innovation
performance
Return on investment in
innovation
Tsai, 2001;Keizer et al., 2002;Flor and Oltra, 2004;
Astebro and Michaela, 2005;Palmberg, 2006;Chiesa
et al., 2009;Sawang, 2011;Idris and Trey, 2011;Caird
et al., 2013;De Felice and Petrillo, 2013;Dewangan and
Godse, 2014;Kim, 2014
R&D costs/revenue in %
Prot margin measures
New-to-market and new-to-
business sales
Percentage of innovations that
met nancial benet
projections
M. Dziallas, K. Blind Technovation xxx (xxxx) xxx–xxx
7
measure innovation because of its availability (Jacobsson et al., 1996).
However, the data on R&D expenses are given in a limited form, as many
companies have strict condentiality policies to secure their competitive
advantage (Kleinknecht, 1993). For small businesses, formal R&D
expenditures are dicult to capture as the R&D budget may be
designated as other expenses (Kleinknecht, 1987). R&D expenditures are
accompanied by newly gained knowledge and by the building of networks
among dierent organizations, research institutions, or universities (Cavdar
and Aydin, 2015). Raymond and St-Pierre (2010) found that process
innovation,such as the improvement of a production method, mediates
the eect of R&D on product innovation (Raymond and St-Pierre, 2010). In
general, high investments in innovations lead to an improved innovation
performance (De Fuentes et al., 2015). Even though the R&D indicators are
a good representation of organizational innovativeness (Romijn and
Albaladejo, 2002), they only provide insights into the innovation input
and not into the specic innovativeness of a company (Godin, 2002).
Therefore, R&D measures innovation indirectly. Moreover, not all
innovations are based on R&D (Becheikh et al., 2006). Generally, R&D
and nancial indicators are represented well in the relevant publications
(11%), and this situation might be due to the fact that these indicators are
basedonnumbersthataremoreaccessible.OnecategoryoftheR&D
dimension is willingness to invest (Astebro and Michaela, 2005). An
indicator for this category is R&D expenditures (Adams et al., 2006).
4.2.1.6. Financial innovation performance. Financial performance is
dened as the earnings of a business through the sale of innovative
products in the market. Financial performance is found to be the third
lowest dimension in the relevant research publications. One reason for
this result is that, although innovation plays an important role in the
success of a business, the actual success that is based on innovation is
dicult to capture. Examples of indicators in this eld are return on
investment with innovations (Kim, 2014) and new-to-market and new-
to-business sales (Caird, Hallett, and Potter, 2013).
4.2.2. Contextual dimensions: Network, market, internationalization, and
environment
Table 4 presents the examples of categories and indicators classied
according to the contextual dimensions that are identied in the innovation
literature between 1980 and 2015. The full list of categories and indicators
of the contextual dimensions is found in the Appendix (Table 11).
The following subsections explain the contextual dimensions in
detail.
4.2.2.1. Network. Generally, a company network includes the
collaboration with external partners, suppliers, and institutions that
are important for the companyinnovation capability. The network
dimension (Fig. 5) is presented the least often at 4% (together with the
strategy dimension). Examples of network categories and indicators are
R&D alliances or co-patents (Adams et al., 2006), cooperation with
universities, research centers, competitors (Keizer et al., 2002), and
customer and supplier relationships (Kamasak, 2015). From the articles
published after 2006, an increase in the openness of companies toward
innovation can be identied (e.g., Alcaide-Marzal and Tortajada-
Esparza, 2007).
4.2.2.2. Market. Market focus plays an important role when aiming for
product success (e.g., Avermaete et al., 2004;Astebro and Michaela,
2005;Bullinger et al., 2007). At the market level, demand and supply
determine the success of a business (Freeman, 1979; Zahra, 1993;
Astebro and Michaela, 2005). The market dimension is the second most
frequently investigated dimension at 13%. Examples of market
categories and indicators are purchase intention rate (Grin and
Page, 1993), sales share of new or highly improved services (%)
(Hollenstein, 2003), and export activities (Martinez-Ros, 1999).
4.2.2.3. Environment. Multiple factors related to internal and external
elements aect the ability of organizations to implement innovations
successfully (Rothwell et al., 1974). The environment as the
surrounding of a business is rarely investigated (5%). The
environment indicators are the number of innovative businesses
(Alcaide-Marzal and Tortajada-Esparza, 2007) and new venture
companies (Al-Mubaraki et al., 2015), among others.
The emphasis of the next section is on product and process in-
dicators and factors to increase the understanding of indicators
throughout the innovation process.
4.3. Findings from the process and product indicator analysis
4.3.1. Innovation process
As described previously, the innovation process (Fig. 6)isusually
complex (Dodgson and Hinze, 2000). To evaluate the indicators relevant
to the mentioned ex-ante view to analyze future success, the innovation
process is divided into dierent stages based on the frameworks of Cooper
(1990) and Hart et al. (2003).Therst stage includes the innovation
strategy as a preparatory step. To categorize the indicators into the dif-
ferent stages and to reduce the complexity, this study assumes the in-
novation process to be linear. Nevertheless, note that the linear portrayal
is dynamic and iterative, with diverse feedback loops to adapt to the
Table 4
Examples of categories and indicators classied according to the contextual dimensions (19802015).
Factors Indicator Relevant references
Market Market demand Demand growth in the industry Freeman, 1979;Zahra, 1993;Crépon et al., 1998;Astebro and
Michaela, 2005Duration of demand
Maintenance and expansion of market share,
growth
Market share, position and share Cooper, 1981;Grin and Page, 1993;Palmberg, 2006;Mendes
Luz et al., 2015
Competitor analysis/monitoring of
competitors
New product introduction vs. competition Lukas and Ferrell, 2000;Ivanova and Avasilcăi, 2014
Customer satisfaction Customer complaints Astebro and Michaela, 2005;Enkel et al., 2005;Chiesa et al.,
2009;Sawang, 2011;De Felice and Petrillo, 2013;Dewangan and
Godse, 2014;Fleuren et al., 2014
Response time to customer requests
Delivery reliability and/or speed
Customer retention rate
Network Internal and external collaboration R&D alliances Flor and Oltra, 2004;Blindenbach-Driessen and van den Ende,
2006;Belitz et al., 2011;Oyelaran-Oyeyinka and Adebowale,
2012;Caird et al., 2013;De Medeiros et al., 2014
Knowledge and technology transfer activities
with research institutions and/or institutions
of higher education
Environment Innovative environment Number of innovative businesses/new
venture start-ups
Alcaide-Marzal and Tortajada-Esparza, 2007;Al-Mubaraki et al.,
2015
Stakeholders Bloch and Bugge, 2013
Political driving forces (e.g. government
stability, taxation policy) and support by
specic policies and programs
Brem and Voigt, 2009;Bloch and Bugge, 2013;Pervan et al., 2015
M. Dziallas, K. Blind Technovation xxx (xxxx) xxx–xxx
8
market requirements (e.g., Kline and Rosenberg, 1986).
1) Strategy: In this phase, the products strategy is dened to achieve a
unique selling proposition.
2) Product denition: With this next step, the product itself is dened
and the market requirements are identied to meet customer ex-
pectations (Lester, 1998). Ideas for innovative products are gener-
ated and evaluated.
3) Product concept: The product concept is created on the basis of the
product denition to coordinate and start the validation and pro-
duction phases. The potential costs and required resources are
considered according to the business case calculations of the in-
novation idea. The development of the product begins.
4) Validation phase: Prototypes are developed and tested to validate
and fulll the diverse requirements.
5) Production phase: When the innovation is produced on a small
scale and the processes are approved, the production of a (pre-)
series starts, and new products are subsequently produced in high
volumes.
6) Market launch and commercialization: The innovations are ready
to be produced in series. The nal products are introduced into the
market with a communication and marketing strategy to achieve the
highest sales gures. For the market launch, dierent indicators
(e.g., the number of products launched) can be found in the selected
literature to measure the innovations (Hittmar et al., 2015).
The innovation process itself is considered a success factor; that is,
the quality of the process aects new product development (Cooper and
Kleinschmidt, 1995). This result is in accordance with the model of
Utterback and Abernathy (1975), which assumes a mutual relation
between the product and the process innovation rate. The model ex-
plains the technological developments, and it dierentiates among
three phases. From the process view, the stages are uncoordinated,
segmental, and systematic. From the product perspective, the stages are
performance maximization, sales maximization, and cost maximization.
In the rst phase, a high product innovation rate accompanies a low
process innovation rate. The process is exible, and a high number of
product variations exist. The innovation competition is high, and the
market share remains low. The second phase is characterized by a de-
creasing product innovation rate and an increasing process rate. At this
point, the process exibility decreases, and higher sales quantities are
produced. Price pressure comes with a growing competition, resulting
in eciency enhancements and rst standardizations. The third phase
refers to the further decreasing product innovation rate that reaches
stagnation and a decreased innovation process rate. This phase signies
a higher product quality, and companies compete for price leadership.
High process standardization implies high product standardization.
Product variations at this level of standardization are cost intensive. In
sum, product and process innovations inuence each other (Utterback
and Abernathy, 1975).
4.3.2. Innovation process indicators
In comparing the process and product indicators shown in Fig. 5,
both process indicators and factors are investigated less frequently (5%)
than product indicators and factors (17%). However, in comparing the
number of unique indicators, their amounts are almost the same. The
relevant unique indicators and factors to evaluate the innovation pro-
cess and products are presented in Tables 5, 6. The tables also show the
characteristics of the indicators, that is, whether the indicators are of
soft, hard, indirect, or direct nature.
Additionally, the innovation process needs to be managed, and its
management mainly involves the planning, supervision, and controlling
of the innovation process. The management of the innovation process is
essential because it aects the success of the innovation process
(Cooper, 1999). Therefore, this category is also included in Table 5.
Examples of process indicators are the time it takes to develop the
next generation of the product (Ivanova and Avasilcăi, 2014), in-
novative activities (Therrien and Mohnen, 2003), and the gap between
plans and action (Kim, 2014).
4.3.3. Product indicators
According to the research literature, several factors are essential for
achieving a high product performance. For example, customers are
crucial for the success of new products. Specically, customer ex-
pectations should be met by adding new or problem-solving functions,
thereby satisfying customer needs (Chiesa et al., 2009; Duhamel and
Santi, 2012; Dewangan and Godse, 2014). This argument implies that
the advantage of an innovative product should be visible to a customer
and the handling of the products functionality should be as intuitive as
possible (Cooper, 1999; Astebro and Michaela, 2005).
Standardization is also important to achieve a good technological
performance (e.g., Blind, 2001;Chiesa et al., 2009). A common in-
dicator for innovation measurements is the number of patents or cita-
tions based on patent data (22 times), which is in accordance with the
results of other studies (e.g., Adams et al., 2006). Patents show a high
potential as a measurements tool. However, using patent data as an
innovation indicator has some limitations (Kleinknecht et al., 2002).
Therefore, the following aspects must be considered: 1) patents protect
inventions and not innovations, 2) not all innovations are patented, and
3) dierent propensities of patenting behavior are dependent on a
companys strategy and sectors (Arundel and Kabla, 1998). In addition
to the third point, the economic value of a patent (Griliches, 1979;
Pakes and Griliches, 1980) or the motives to patent (Blind et al., 2006)
should be considered when using these data for measuring innovation.
Patents are used for strategic purposes, such as to block other patents
with unused patents or to receive licensing fees (Torrisi et al., 2016).
Furthermore, from a strategic viewpoint, patents are used to improve a
companys position compared with its competitors or in negotiations
with licensees or partners. Patents also play a role as an indicator for
performance measures and incentives for R&D personnel (Blind et al.,
2006). Furthermore, the motives to patent and the number of sleeping
(unused) patents are positively correlated with company size (Blind
et al., 2006; Torrisi et al., 2016). Despite these limitations of patents as
an innovation indicator, patents represent new technologies. As patents
are correlated with innovation activity (Acs et al., 2002), they can in-
dicate innovations. They also provide helpful hints to understand the
innovation processes within international research interactions (Stek
and Geenhuizen van, 2015).
The important product innovation indicators are the number of new
product ideas (Cooper and Kleinschmidt, 1993), future duration of
products (Astebro and Michaela, 2005), and number of project deni-
tions with business approval (Tipping et al., 1995). To evaluate ideas
for innovations, these indicators may play an important role in the
product denition and concept phase. Furthermore, a precise, stable,
and early product denition before development starts (Cooper, 1999)
is important for the subsequent development of a product, and thus it
could be used as an indicator. Moreover, the innovation portfolio bal-
ance may be essential from a strategic point to achieve high success
rates with innovations (Adams et al., 2006; Kim, 2014). After market
launch, the new product performance or the success rate of new pro-
ducts becomes an important indicator. It can be measured by the per-
centage of innovations that met the nancial prot, the protability of
newly listed products, and the number of products launched within the
last three years (Grin and Page, 1993; Chiesa et al., 2009; Tohidi and
Jabbari, 2012; Edison et al., 2013; Ivanova and Avasilcăi, 2014; Kim,
2014; Hittmar et al., 2015), among others. Product advantage (Cooper,
Fig. 6. Simplied innovation process based on Cooper (1990) and Hart et al.
(2003).
M. Dziallas, K. Blind Technovation xxx (xxxx) xxx–xxx
9
Table 5
Process innovation indicators and factors identied in the relevant literature and categorized under the innovation process (19802015).
Indicators and factors Relevant references s/h i/d
Product
denition
Time to develop next generation Ivanova and Avasilcăi (2014) hd
Diversity of idea-generation process Kim (2014) si
Time for idea generation Lester (1998) hi
Idea-tanks management Yang et al. (2015) sd
Idea generation and its management Koc (2007),Escalfoni et al. (2011) sd
Motivational factors in the work environment to generate ideas Foss et al. (2013) sd
Product development planning and process March-Chordà et al. (2002) si
Business planning Aiman-Smith et al. (2005) si
Involvement of product champions/% of projects for which an eective project
champion can be identied on the project team
Tipping et al. (1995), Blindenbach-Driessen and van den Ende
(2006)
hi
Product concept Number of on-going innovations Hittmar et al. (2015) hd
Innovation activity Therrien and Mohnen (2003) sd
Explicit project selection Blindenbach-Driessen and van den Ende (2006) si
Design orientation, such as, number of designers on the companys sta,or
source of design
Alcaide-Marzal and Tortajada-Esparza (2007) hi
Making business cases Blindenbach-Driessen and van den Ende (2006) si
Validation phase Innovative projects (in-house) Caird et al. (2013) si
Detailed project tactical plan Lester (1998) si
Formal ratication by management Fleuren et al. (2014) si
Implementation of innovation activity Belitz et al. (2011) sd
Motivational factors in the work environment to implement ideas Foss et al. (2013) sd
Gap between plans and action Kim (2014) hd
Production phase Cost of production/new product developments Astebro and Michaela (2005) hi
Ease of manufacture Grin and Page (1993) si
Manufacturing eciency and productivity Grin and Page (1993), Pachico (1996),Palmberg (2006),Han
et al. (2009),Damijan et al. (2012),Hittmar et al. (2015)
hd
Increase in production capacity and exibility Mendes Luz et al. (2015) sd
Internal process lead time Han et al. (2009) hi
Number of optimized production processes that the company used for its products Tohidi and Jabbari (2012) hi
Use of new technology for production of new products Tohidi and Jabbari (2012) si
Planning and manufacturing system Yang et al. (2015) si
Reassessment eorts: update and redirect project plans and keep team
members aligned
Lester (1998) si
Time to implement the innovation Fleuren et al. (2014) hd
Process time Sawang (2011) hi
Market launch Time from identication of a customer product need until beginning of
commercial
Tipping et al. (1995) hd
Time to market Adams et al. (2006),Chiesa et al. (2009),De Felice and Petrillo
(2013),Edison et al. (2013),Hittmar et al. (2015)
hd
Building lead time Han et al. (2009),Sawang (2011) hi
Labor productivity Sawang (2011) hi
Number of improved processes Hittmar et al. (2015) hi
Percentage decrease in the cost of innovative processes and products Hittmar et al. (2015) hd
Percentage of project milestones achieved Tipping et al. (1995) hi
Duration of introduction of product Escalfoni et al. (2011) hd
Indented purpose achievement of innovation Astebro and Michaela (2005) si
A well-planned, adequately resourced and prociently executed launch Cooper (1999) sd
Determinants related to facilities that are needed to implement the innovation Fleuren et al. (2014) si
Total cost of all commercially successful projects divided by the number of
commercially successfully projects
Tipping et al. (1995) hd
Innovation
process
management
Project eciency in relation to cost and time Chiesa et al. (2009) hd
Measurement of time Grinand Page (1993) hi
Project delay Feurer et al. (1996) hd
Rate of received approval on time Han et al. (2009) hi
Quality of execution of the activities that comprise the innovation process Cooper and Kleinschmidt (1993) si
High-quality new product process Cooper and Kleinschmidt (1995) si
Ecient processes like tough go/kill decision points or gates Cooper (1999) si
New product development process/process management itself Lester (1998),Slater et al. (2014), Raja and Wei (2015) si
Heavyweight project management Blindenbach-Driessen and van den Ende (2006),Lene (2008) si
Percentage of projects in the total portfolio going through a dened project
management system with dened milestones
Tipping et al. (1995) hi
Clear goals and milestone measurements Lester (1998) si
Percentage of completion of objectives at the expected milestone date Tipping et al. (1995) hi
Planning and monitoring of the innovation process Huergo (2006) si
Way in which the new product development process is formalized Graner and Mißler-Behr (2013) si
Common understanding of the process for new product development Lester (1998) si
Procedural clarity Fleuren et al. (2014) si
Project ownership/empowerment (=support and freedom) Tipping et al. (1995) sd
Flexibility and agility, such as centralization of decision-making Koberg et al. (1996) si
Feedback eects in-between innovation decisions Martinez-Ros (1999) si
Responsibility Gana (1992) si
Project leader Flipse et al. (2013) si
Replacement for leaving staFleuren et al. (2014) si
Monitoring of the innovation process and the hiring of personnel with special
skills for technological tasks
Huergo (2006) si
Number of meetings De Felice and Petrillo (2013) hi
s = soft (qualitative indicator)|h=hard (quantitative indicator).
d=direct inuence on innovation success|i = indirect inuence on innovation success.
M. Dziallas, K. Blind Technovation xxx (xxxx) xxx–xxx
10
Table 6
Product innovation indicators and factors identied in the relevant literature and categorized under the innovation process (19802015).
Indicators and factors Relevant references s/h i/d
Product denition Number of new product ideas or suggestions Cooper and Kleinschmidt (1993), Chiesa et al. (1996),Chiesa et al.
(2009),Dewangan and Godse (2014)
hd
Percentage of ideas found viable for commercialization Dewangan and Godse (2014) hd
Precise, stable and early product denition, before development begins Cooper (1999) si
Synergy potential/dependency on other products Grin and Page (1993), Astebro and Michaela (2005) si
Innovation portfolio balance Adams et al. (2006), Kim (2014) si
Newness to company and novelty of product Cooper (1981),Duhamel and Santi (2012) sd
Future duration of product Astebro and Michaela (2005) sd
Product concept Intellectual property rights, in particular patents, citations, applications,
licenses
Basberg (1987), Acs and Audretsch (1989), Dror (1989),Griliches
(1990), Trajtenberg (1990), Brouwer and Kleinknecht (1991),Grupp
(1992),Jae et al., (1992, 1993),Shane (1993),Littell (1994),
Geisler (1995), Tipping et al. (1995),Macdonald and Lefang (1998),
Veugelers and Cassiman (1999), Katila (2000),Blind (2001),Acs
et al. (2002),Beneito (2003), Hagedoorn and Cloodt (2003);
Hollenstein (2003),Therrien and Mohnen (2003),Czarnitzki and
Kraft (2004), Flor and Oltra (2004),Lanjouw and Schankerman
(2004),Astebro and Michaela (2005), Hipp and Grupp (2005),
Adams et al. (2006), Lin and Lu (2006), Mattes et al. (2006),Alcaide-
Marzal and Tortajada-Esparza (2007),Tseng and Wu (2007),
Gittelman (2008),Chiesa et al. (2009),Ejermo (2009), Yunwei et al.
(2009),Buesa et al. (2010),Guan and Chen (2010),Bayarçelik and
Taşel (2012), Adams et al., 2013; Belenzona and Patacconi (2013);
De Rassenfosse et al. (2013), Dereli and Altun (2013), Karvonen and
Kässi (2013),Makkonen and van der Have (2013),Dewangan and
Godse (2014), Kim (2014), Sosnowski (2014),Thoma (2014),Akis
(2015),Al-Mubaraki et al. (2015),Cavdar and Aydin (2015), Dang
and Motohashi (2015), Hittmar et al. (2015), Raja and Wei (2015),
Rocha et al. (2015),Roper and Hewitt-Dundas (2015)
hi
Power law distributions of patents ONeale and Hendy (2012) si
Technological signicance Astebro and Michaela (2005) si
Technical standards and standardization degree Blind (2001), Chiesa et al. (2009) hi
Number of project denitions with business/marketing approval Tipping et al. (1995) hd
Validation phase – –
Production phase – –
Market launch New product performance/ success rate of new products, e.g, percentage of
innovations that met nancial prot estimates, protability of newly listed
products, number of products launched (last three years)/output quantity
Grin and Page (1993), Han et al. (2009), Dewangan and Godse
(2014), Hittmar et al. (2015)
hd
Number of new or improved products within a certain period, or number of
new products divided by total number of products oered
Grin and Page (1993), Therrien and Mohnen (2003),Hipp and
Grupp (2005), Alcaide-Marzal and Tortajada-Esparza (2007),Chiesa
et al. (2009),Tohidi and Jabbari (2012),Edison et al. (2013),Ivanova
and Avasilcăi (2014), Kim (2014), Hittmar et al. (2015)
hd
Counts of new product announcements, introduction of technological
innovations to the market
Beneito (2003), Mendes Luz et al. (2015) hd
Successful idea implementation, such as number of ideas successfully
turned into products or shared ideas submitted to successful ideas
Edison et al. (2013),Hittmar et al. (2015) hd
Identication of innovations by business managers Flor and Oltra (2004) si
Assessments by experts Kleinknecht and Reijnen (1993) si
Perceived value Grin and Page (1993) sd
Survival rate Grin and Page (1993), Al-Mubaraki et al. (2015) hd
Straightforwardness (how easily can the customer learn the correct use of
the innovation?)/customer familiarity with the innovation and
specialization
Cooper (1981), Astebro and Michaela (2005), Duhamel and Santi
(2012)
sd
Product advantage: dierentiated, unique benets, superior value for the
customer, recognizably of advantage
Cooper (1981, 1999),Astebro and Michaela (2005) sd
Degree of innovativeness De Brentani (2001) sd
Appearance Astebro and Michaela (2005) sd
Awareness of content of innovation Fleuren et al. (2014) sd
Product quality and reliability, such as customer evaluation/defect rate
assessment
Grin and Page (1993), Tipping et al. (1995), Baldwin and Johnson
(1996),Sawang (2011), De Felice and Petrillo (2013),Edison et al.
(2013),Mendes Luz et al. (2015)
hd
Comparative functionality Astebro and Michaela (2005) si
Compatibility, correctness, completeness Astebro and Michaela (2005), Fleuren et al. (2014) si
Complexity of innovation Fleuren et al. (2014), Findik and Beyhan (2015) si
Societal benetAstebro and Michaela (2005),Chiesa et al. (2009) si
Opportunity window Cooper and Kleinschmidt (1987) si
Launch quality and quantity Cooper and Kleinschmidt (1993) hd
Duration of product life cycles Hittmar et al. (2015) hd
Sustainability/eco-eciency of product Kobayashi (2006) sd
Extending the range of oered products Mendes Luz et al. (2015) hi
Numerical rankings of a businessproduct by a given customer divided by
that customer's ranking of the best competitive product
Tipping et al. (1995) hi
% of sales protected by patents, trade secrets, other exclusive know-how Tipping et al. (1995) hi
Licensed technology use Güngör and Gözlü (2012) hi
Trademarks Schmoch and Gauch (2009),Flikkema et al. (2014) sd
s = soft (qualitative indicator) | h = hard (quantitative indicator).
d=direct inuence on innovation success | i = indirect inuence on innovation success.
M. Dziallas, K. Blind Technovation xxx (xxxx) xxx–xxx
11
1999) is important to attain competitive advantage, which shows un-
ique benets and a superior value to customers. Table 6 presents the
complete list of identied product innovation indicators.
4.3.4. Ex-ante versus ex-post indicators
In this section, the identied indicators and factors are classied
into the stages of the innovation process. Fig. 7 shows the number of
identied unique indicators throughout the innovation process.
As shown in Fig. 7, the product denition phase presents 16 in-
dicators, and the product concept phase (ex-ante) shows 10 indicators
and factors. The validation and production phases even show a com-
plete lack of product innovation indicators. The market launch phase
(ex-post) presents the highest number of indicators and factors (39). In
total, 82 indicators are found in the relevant publications.
The early stages of the innovation process show fewer indicators
and factors than the end of the innovation process (market launch). One
reason for this may be that vague ideas and concepts are dicult to
evaluate in the beginning of the process (Kim and Wilemon, 2002). This
may also be the reason why more qualitative indicators are represented
in the early phases (Fig. 8), as capturing quantities at the beginning of
the innovation process is more dicult. Intellectual property rights,
mostly patent data, are frequently investigated in the research literature
as a way to measure innovation. Depending on a companys expertise,
patents are applied in the concept phase to ensure the freedom to op-
erate (Ernst, Conley, and Omland, 2016). Moreover, indicators for
strategyare lacking in the identied literature.
The results highlight that the number of indicators for the later
stages exceeds the number of indicators for the early stages of the in-
novation process. The results also point out that the more progressed
the innovation process is, the higher the number of product indicators.
This outcome can be explained by the fact that with the nalized pro-
duct, more data are available to evaluate the product.
The ex-ante view plays a signicant role in the evaluation of deci-
sion-making processes, which can be linked to ex-post results (e.g., Potì
and Cerulli, 2011). Therefore, focusing on the ex-ante and ex-post in-
dicators is important. Ex-ante decision making refers to phases one to
three of the innovation process, which precede the validation phase and
in which the product is dened and conceptualized. Changes are still
easier to implement in the early phases than in the validation stage or
subsequent stages. Innovative ideas need to be evaluated in terms of
their potential success in the future. Although potential success is dif-
cult to assess, decisions about which ideas or projects should be
Fig. 7. Indicators and factors throughout the innovation process (19802015).
Fig. 8. Indicators throughout the innovation process categorized into qualita-
tive, quantitative, direct, and indirect indicators (19802015).
Table 7
Most frequently used ex-ante and ex-post indicators and factors (19802015).
Ex-ante indicators Selected references Ex-post indicators Selected references
Product indicators Patents/patent applications Chiesa et al. (2009), Makkonen and van der Have (2013),
Rocha et al. (2015),Roper and Hewitt-Dundas (2015)
Straightforwardness (how easily can the customer learn the correct
use of the innovation?)
Astebro and Michaela (2005)
Customer focus Raja and Wei (2015) Count of new product announcements Beneito (2003)
Number of ideas Chiesa et al. (2009),Hittmar et al. (2015) Number of new products Chiesa et al. (2009)
Percentage of ideas found viable for
commercialization
Dewangan and Godse (2014) Product advantage, dierentiated, unique benets, superior value for
the customer
Cooper (1999)
Future duration of product, technology
signicance
Astebro and Michaela (2005) Degree of innovativeness De Brentani (2001)
Dependency on other products Astebro and Michaela (2005) Number of improvements in existing products Edison et al. (2013)
Novelty to the company Duhamel and Santi (2012) Success rate of new products and rate of survival on market Grin and Page (1993)
Shared ideas submitted and successful ideas Hittmar et al. (2015)
Process indicators Time for idea generation Lester (1998) Time taken in turning an idea into a product or market launch Adams et al. (2006);Edison
et al. (2013)
Number of on-going innovations/ innovation
activity
Therrien and Mohnen (2003),Hittmar et al. (2015) Rate of suggestions implemented Chiesa et al. (2009)
Idea generation Koc (2007) Total cost of all commercially successful projects divided by the
number of commercially successful projects
Tipping et al. (1995)
Idea management Escalfoni et al. (2011) Number of improved processes Hittmar et al. (2015)
Precise, stable and early product denition before
development begins
Cooper (1999) Time from identication of a customer product need until
commercial sales
Tipping et al. (1995)
Business planning Aiman-Smith et al. (2005) ––
Time to develop next generation Ivanova and Avasilcăi (2014) ––
M. Dziallas, K. Blind Technovation xxx (xxxx) xxx–xxx
12
focused on need to be made during the early process stage. Nowadays,
in the validation phase, projects are halted because of the lack of
technical or production feasibility. Table 7 shows the most frequently
used ex-ante and ex-post indicators and factors.
Although some ex-ante indicators can be identied, more indicators
for the ex-ante evaluations of innovations in policymaking and orga-
nizational contexts seem to be necessary to better estimate the potential
of innovations. This overview shows that ex-post indicators mostly refer
to the innovation performance in terms of quantitative values.
However, exactly assessing the innovation success ex-post remains
dicult. In addition, the benchmark of indicator values deserves fur-
ther research to benet from the application of indicators in practice.
Nevertheless, each institution or company should predene the values
that it aims to reach per observation unit. In other words, the indicator
benchmark depends on the corresponding industry. For example, the
same values of indicators for the software branch might not as im-
portant as they are for the automotive industry to the same extent.
4.3.5. Characteristics of product and process indicators: qualitative and
quantitative as well as direct and indirect
In this subsection, the research results about the product and pro-
cess indicators are outlined in terms of their quantitative and qualita-
tive nature (Hoelscher and Schubert, 2015), as illustrated in Fig. 8.
Moreover, the direct and indirect nature of an indicator, which illus-
trates its inuence on an innovations success, is shown. As mentioned
previously, this study considers the term successto mean high sales
gures (Astebro and Michaela, 2005).
As shown through a comparison of the dierent phases of the in-
novation process, the product denition phase shows more qualitative
(soft) than quantitative (hard) indicators. This result could be a sign
that using quantitative values to assess vague innovative ideas at the
beginning of the innovation process would be dicult. At this point,
indirect and direct indicators show the same numbers. Remarkably, in
the product concept phase, the quantitative and qualitative indicators
are found to be equally distributed in the selected literature, whereas
the indirect indicators exceed the direct indicators. The validation
phase shows a higher number of qualitative than quantitative indicators
and factors. In the production phase, quantitative and indirect in-
dicators exceed the number of direct indicators. The higher indirect rate
of indicators could exist because the indicators that monitor the process
itself are more important during the production phase than the in-
dicators related to the success of the product. The market launch phase
shows the highest number of indicators. In this phase, the quantitative
and direct indicators are the most represented indicators reviewed lit-
erature, closely followed by the qualitative and indirect indicators
(Fig. 8).
Tables 8, 9 provide further insights into the indicator character-
istics. The results reveal that the number of unique process indicators
slightly exceeds the number of unique product indicators. This outcome
emphasizes the notion that the innovation process itself inuences the
innovative product (Utterback and Abernathy, 1975). A high-quality
innovation process accompanies a high-quality product. However, the
dierences between the process and the product indicators are only
minor. Indicators help to manage the process and to achieve the dened
goals. The general indicators for evaluating the innovation manage-
ment process are listed here separately because categorizing these in-
dicators into only one phase is impossible.
The results show that more qualitative (61) than quantitative (45)
indicators are mentioned in the relevant literature. One reason for this
nding is that innovations are dicult to depict as concrete values,
especially at the beginning of the innovation process. Additionally,
there are fewer direct (42) indicators than indirect (64) indicators.
Therefore, the indicators that directly inuence the innovation success
are underexplored to some extent. This result underlines the need for
further studies on direct indicators. The qualitative indicators may be
sucient to evaluate innovations in the early stages of the innovation
process because the actual number of innovations is rare in this initial
phase. Nevertheless, further investigations are required to identify the
relevant qualitative indicators that can be used by researchers, man-
agers, and policymakers. To summarize, the qualitative and indirect
indicators are investigated more than the quantitative and direct in-
dicators. However, this needs to be further analyzed and tested.
Specically, process indicators show more quantitative and indirect
indicators than product indicators, as shown in Table 9. The reason for
this nding may be that more general project management indicators
and factors are known for the innovation process. The qualitative
Table 8
Overview of the indicators categorized into quantitative and qualitative as well
as direct and indirect (19802015).
Hard Soft Direct Indirect Total
Combinations xx 22
xx 20
xx23
xx41
Total 45 61 42 64
soft=qualitative indicator|hard=quantitative indicator.
direct=direct inuence on innovation success|indirect=indirect inuence on
innovation success.
Table 9
Overview of the product and process indicators and factors categorized into hard and soft as well as direct and indirect nature throughout the innovation process
(19802015).
Product Process
Soft Hard Direct Indirect Soft Hard Direct Indirect Total
Product denition 52 4 3 63 4 5 16
Product concept 23 1 4 32 2 3 10
Validation phase 00 0 0 51 3 3 6
Production 00 0 0 56 3 8 11
Market launch 15 12 16 11 3 9 5 7 39
General innovation management 17 7 4 20 24
soft=qualitative indicator|hard=quantitative indicator.
direct=direct inuence on innovation success|indirect=indirect inuence on innovation success.
M. Dziallas, K. Blind Technovation xxx (xxxx) xxx–xxx
13
factors and indicators are equally represented with regard to the pro-
duct and process indicators. Direct product indicators and factors are
mentioned more than the direct process indicators. For product in-
dicators, a large number of indicators and factors have already been
published, but more specic product indicators are needed to evaluate
innovations.
In comparing the process stages, the results highlight that the
number of soft indicators exceeds the number of hard indicators in the
early stages of the innovation process. This outcome can be explained
by the fact that at the front-end, the exact number of ideas is dicult to
generate. Moreover, the number of indirect indicators exceeds the
number of direct indicators in the early stages of the innovation pro-
cess.
Regarding the characteristics of innovation indicators, a change to a
higher number of soft indicators is observable. These soft indicators
refer to non-technological and technological innovations as well as a
broader spectrum of industries (including service industries) and a
wider timeframe. However, further investigation on indicators is re-
quired to generalize the results.
Although a notable set of indicators is known, the actual indicators
to assess the potential of innovation remain mostly missing probably
because of the described lack of particular data necessary to evaluate
innovations. Especially at the beginning of the innovation process, es-
timating the future success of innovations, particularly without su-
cient data, is challenging. Furthermore, the innovations and their in-
uencing factors may vary in unforeseeable ways. Therefore, evaluating
innovations in the early stages of new product development is proble-
matic.