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SHORT-, MID-, AND LONG-TERM EFFECTS OF INNOVATION ACTIVITIES: A CONFIGURATIONAL ANALYSIS ON CONTINUITY, COMPETENCE, AND COOPERATION

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International Journal of Innovation Management
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The constant generation of innovation is a major factor in explaining a firm’s long-term success. Accordingly, previous literatures have identified several organisational, processual, and cultural factors that enable firms to promote successful innovation. Although these success factors appear to be rather different, most of them revolve around continuity, competence, or cooperation. As little prior research has focused on the complexity and interdependence of these various interlinked theoretical concepts, we adopt a configurational and longitudinal approach to analyse the effect of continuity, competence, and cooperation on the innovation performance of a firm on short-, mid-, and long-term bases. Based on a longitudinal data set that captures the innovation behaviour of 220 firms from 2009 to 2015, we find that continuity is the basic requirement for constant innovation performance. In addition, cooperation is likely to be supportive of innovation performance in the short term, while competence supports innovation performance in the long term.
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SHORT-, MID-, AND LONG-TERM EFFECTS OF INNOVATION
ACTIVITIES: A CONFIGURATIONAL ANALYSIS ON
CONTINUITY, COMPETENCE, AND COOPERATION1
Michael Kötting (corresponding author)
University of Hohenheim, Wollgrasweg 49, 70599 Stuttgart, Germany.
Mail: michael.koetting@zeb.de
Andreas Kuckertz
University of Hohenheim, Wollgrasweg 49, 70599 Stuttgart, Germany.
Mail: andreas.kuckertz@uni-hohenheim.de; Phone: +4971145924820
ACKNOWLEDGMENTS
We appreciate access to the Mannheim Innovation Panel provided by the ZEW—Leibniz Centre for Euro-
pean Economic Research.
1 This is a preprint version. For the final version refer to “Short-, mid-, and long-term effects of innovation activities:
A configurational analysis on continuity, competence, and cooperation”. M. Kötting & A. Kuckertz. International
Journal of Innovation Management. https://doi.org/10.1142/S1363919621500535
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AUTHOR CONTRIBUTIONS
Michael Kötting: Conceptualization, Formal Analysis, Investigation, Data Curation,
Writing—Original Draft, Project Administration. Andreas Kuckertz: Conceptualization, Validation, Writ-
ing—Review and Editing, Supervision.
ABSTRACT
The constant generation of innovation is a major factor in explaining a firm’s long-term success. Accord-
ingly, previous literature has identified several organizational, processual, and cultural factors that enable
firms to promote successful innovation. Although these success factors appear to be rather different, most
of them revolve around continuity, competence, or cooperation. As little prior research has focused on the
complexity and interdependence of these various interlinked theoretical concepts, we adopt a configura-
tional and longitudinal approach to analyse the effect of continuity, competence, and cooperation on the
innovation performance of a firm on short-, mid-, and long-term bases. Based on a longitudinal data set that
captures the innovation behaviour of 220 firms from 2009 to 2015, we find that continuity is the basic
requirement for constant innovation performance. In addition, cooperation is likely to be supportive of in-
novation performance in the short term, while competence supports innovation performance in the long
term.
KEYWORDS
Innovation performance; continuity; competence; cooperation; qualitative comparative analysis; fsQCA
1. INTRODUCTION
Current developments such as the increasing digitalization and globalization of the economy are accelerat-
ing the dynamics of markets, technologies, and innovation (Abernathy & Utterback, 1978; Tushman &
Anderson, 1986). In order to sustain their position in such an environment, established firms in particular
must continually adapt to their surroundings and be able to generate new competitive advantage, particularly
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in the form of innovative technologies and products (Berger et al., 2019). However, doing so involves the
continuous assessment of the environment; thus, determining appropriate strategies requires great effort and
discipline (Bower & Christensen, 1995). How challenging this is can be demonstrated by examining numer-
ous examples in practice, as in the recent past, numerous formerly very successful—occasionally even market-
leading—firms did not anticipate new relevant developments at an early stage; this resulted in their market
position being significantly threatened (Christensen, 1997; Christensen & Raynor, 2003).
Although the generation of innovation requires enormous effort and is a rather complex task, there are nu-
merous academic studies that have identified respective success factors (e.g. Evanschitzky et al., 2012;
Keupp et al., 2012; Slater et al., 2013; West & Bogers, 2014). Most of these success factors can be traced
back to the concepts of continuity (e.g. Hargadon, 1998; Karlsson & Björk, 2017; Soosay & Hyland, 2008;
Steiber & Alänge, 2013; Verona, 2003), competence (e.g. Acur et al., 2010; Atuahene-Gima & Wei, 2011;
Danneels, 2002, 2008; Reid & Brentani, 2010), and cooperation (e.g. Barge-Gil, 2010; Belderbosa et al.,
2004; Faria et al., 2010; Kim & Yoon, 2019; Knudsen, 2007). Continuity describes the ability of a corpo-
ration to constantly and systematically transform knowledge into innovations based on defined processes
and procedures (Hargadon, 1998). With the organizational implementation of such processes in a firm, these
processes can be executed in a repeatable manner, so that innovations can be produced on a continuous basis
(Boer & Gertsen, 2003; Steiber & Alänge, 2013). The concept of competence subsumes different types of
internal knowledge (e.g. management knowledge, market knowledge, and technical knowledge), which is
the starting point for the internal generation of innovation (Darroch, 2005; Herstad et al., 2015; Quintana-
García & Benavides-Velasco, 2008). While competence is based on the internal knowledge base of a firm,
cooperation describes the collaboration with strategic partners to acquire their knowledge and using that
knowledge to expand the acquiring firm’s internal knowledge base (Cohen & Levinthal, 1990; Ireland et
al., 2002; Mowery et al., 1996). Therefore, cooperation is an important complement to the internal genera-
tion of new knowledge.
Even though the concepts have been explained separately in the literature, they are highly dependent. For
example, although a continuous process for the generation of innovations (continuity) is established within
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a corporation, this process will only generate successful innovations if the corporation has access to a suf-
ficient internal knowledge base (competence) as input for this process (Jantunen, 2005; Zhou & Li, 2012).
However, for the development of radical innovations, additional (complementary) knowledge is required,
which is often acquired from external sources (cooperation) (Cassiman & Veugelers, 2006; Chesbrough et
al., 2006; Ebersberger et al., 2012). Thus, the three concepts are strongly interlinked and have various inter-
dependencies. Therefore, it is necessary to analyse them in combination and consider the interaction between
the concepts as well as their combined effect on a corporation’s innovation performance.
While innovation research was initially largely explorative, the literature reviews by Keupp et al. (2012)
and Savino et al. (2017) observe that the number of confirmative statistical studies with large data sets has
recently undergone a notable increase (e.g. Allmendinger & Berger, 2020; Arbussã & Llach, 2018; Diener et
al., 2020; Doran et al., 2019; Doran et al., 2020; Dutta & Rousseau, 2020; Kruft & Kock, 2019; Moretti &
Biancardi, 2020; Moura et al., 2020; Wen et al., 2020). Although these studies are informative, the standard
methods utilized there (e.g. linear regression) have the disadvantage that they often only analyse the effect
of various independent variables on the dependent variable. However, complex relationships between the
independent variables and their effect on an outcome often cannot be adequately captured (Berger & Kuck-
ertz, 2016; Feurer et al., 2016; Gligor et al., 2019; Greckhamer et al., 2008; Greckhamer et al., 2018; Ho et
al., 2016; Vis, 2012; Woodside, 2010, 2013). Qualitative methods (e.g. multiple case studies), which are
often capable of revealing such complex relationships, can usually only be conducted with small to medium
case numbers (Yin, 2003). Therefore, this study is based on the claim to investigate the interaction between
continuity, competence, and cooperation as well as their short-term and long-term effects on innovation
performance on the basis of a larger data set. Thus, we adopt an inductive configurational and longitudinal
approach to analyse the effects of continuity, competence, and cooperation on the innovation performance
of a corporation on a short-, mid-, and long-term basis. This choice was motivated by configurational anal-
ysis being an appropriate tool to capture complex relationships between causally relevant conditions and a
specified outcome (Kraus et al., 2018).
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This leads us to our research question: How do continuity, competence, and cooperation translate into
innovation performance? Although several researchers have already applied a configurational approach
within the innovation management literature (Berger, 2016; Kraus et al., 2018), to the best of our
knowledge, this is the first study to analyse the effects of continuity, competence, and cooperation on inno-
vation performance through a configurational approach.
Such a use of a configurational approach to reveal the interactions of continuity, competence, and coopera-
tion and the impact on the innovation performance of a firm constitutes this study’s first contribution to the
academic literature. The second contribution is derived from the current research being longitudinal and,
thus, examining short-term results alongside those in the mid-term and long-term, which implies the study
can develop and support propositions regarding lead times and the sustainability of various concepts (conti-
nuity, competence, cooperation). Overall, therefore, the current research helps to fill the identified gap in
the literature to discover the complex relationship of continuity, competence, and cooperation and how
these impact a corporation’s innovation performance in the short- and long-term.
2. THEORETICAL BACKGROUND
2.1. Continuity
Owing to the proliferation of market and technological changes, firms are now confronted with an increas-
ingly competitive market environment. In order to successfully differentiate themselves in such an environ-
ment and maintain competitive advantage, firms must develop innovation by continuously adapting to mar-
ket conditions and technological changes (Nelson, 1991; Tushman & Anderson, 1986), a necessity that is
also reflected in the development of the innovation literature. While innovation literature in the early 1970s
focused on the success factors underlying individual innovation projects (e.g. Myers & Marquis, 1969; Roth-
well, 1977), during the 1980s, the literature developed to embrace a more process-oriented perspective (e.g.
Burgelman, 1983; Cooper, 1983; Tushman & Nadler, 1986); then, from the mid-1990s onward, new studies
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emerged that proposed that a focus on a single innovation project would not be sufficient to remain compet-
itive. This wave of research suggested that sustainable success is significantly more dependent on organiza-
tional processes and procedures to develop multiple innovation projects in a standardized and repetitive
manner (e.g. Brown & Eisenhardt, 1997; OConnor, 2008; OConnor & DeMartino, 2006; Kötting & Kuck-
ertz, 2019).
Following the development of the innovation literature, numerous studies from the 2000s address the topic
of continuous innovation (e.g. Hargadon, 1998; Boer & Gertsen, 2003; Steiber & Alänge, 2013; Xu et al.,
2010). Most of these studies were grounded on innovation and knowledge management principles and focus
on the repetitive execution of innovation activities and, thus, relate to (continuous) improvement and (con-
tinuous) learning. Many concluded that the continuous practice of these activities could lead to sustainable
innovation performance (e.g. Boer & Gertsen, 2003). Therefore, continuous innovation can be regarded as
a specific operationalization (so-called micro-foundations) of the dynamic capability concept (e.g. Björk et
al., 2010; Verona, 2003). Dynamic capabilities are those capabilities of a firm that enable it to continuously
integrate, build up, and reconfigure knowledge to deal with a rapidly changing environment (Teece et al.,
1997).
Although empirical studies on the approach to continuous innovation are not yet widespread (Boer
& Gertsen, 2003), other innovation studies show that the development, adoption, and commercialization of
innovation is associated with a lead time that can extend to several years (Brouwer & Kleinknecht, 1999;
Himmelberg & Petersen, 1994; Lindner, Fischer, & Pardey, 1979; Mansfield, 1991, 1998). Therefore, if
innovation activities do not occur on a continuous basis, appropriate lead times for knowledge acquisition
and application must be provided for each time that the activities are readopted (Ravenscraft & Scherer,
1982). Considering that market and technological changes usually occur in cycles (Abernathy & Utterback,
1978; Gersick, 1991; Tushman & Anderson, 1986), a short-term reaction to these changes is often not pos-
sible when practicing innovation activities on a discontinuous basis. Doing so can lead to innovation activ-
ities being initiated at the high point of a cycle and materializing at its low point, which is often too late
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(Bower & Christensen, 1995). Assimilating the above arguments, we conclude that continuity is a key ele-
ment for the sustainable generation of innovation.
2.2. Competence
Competence has long played a major role in management and innovation literature. In the early 1990s,
several studies within the management literature argued that the success of a firm is usually based on its
unique set of core competencies (e.g. Prahalad & Hamel, 1990), which serve to differentiate the firm and
its products from its competitors and, therefore, must be strengthened by the firm’s management (Coombs,
1996; Javidan, 1998; Prahalad, 1993). Subsequent research, which increasingly scrutinized the dynamics
of technological changes and shifts in market conditions, elaborated the risk of maintaining an overly strong
focus on core competencies (Bower & Christensen, 1995). This stream of research argues that an exces-
sively narrow focus on existing core competences invites the risk of falling into a competence trap
(Dougherty, 1995; March, 1991; Sirén et al., 2012)—a situation in which firms concentrate too strongly on
their existing competences and products but neglect the generation of new competences and innovation
(Bower & Christensen, 1995; McNamara & Baden-Fuller, 1999; Raisch & Birkinshaw, 2008). Therefore,
taking the risk of a competence trap into account, we do not define competence solely as the existing
knowledge base of a corporation but instead also as the ability to monitor, evaluate, integrate, and leverage
new knowledge to generate innovation (Quintana-García & Benavides-Velasco, 2008; Teece et al., 1997).
The competence of a firm to generate innovation is mainly grounded in it developing a comprehensive
knowledge base. Therefore, a firm’s knowledge base must be diverse to a certain extent in order to provide
a broad variety of new knowledge to be monitored and evaluated (Katila & Ahuja, 2002; Quintana-García
& Benavides-Velasco, 2008). Therefore, the firm must acquire a knowledge inventory that is applicable in
the future, even though it is not possible for its management to know in advance exactly what this inventory
will be employed for (Levinthal & March, 1993). Having such an inventory not only prepares firms to react
appropriately to future developments but also strengthens their absorptive capacity, which enables them to
better integrate and leverage new knowledge as a basis for innovation (Cohen & Levinthal, 1990). If a
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firm’s management, for example, does not have fundamental knowledge of an upcoming technology, it will
not be able to evaluate that technology and, thus, will not be able to make an informed decision regarding
its relevance. Therefore, a comprehensive and diverse knowledge base plays an essential role in preventing
the core rigidities for firms, because it enables management to effectively assess upcoming developments
(Leonard-Barton, 1992). Thus, the regular addition of new knowledge to a firm’s knowledge base is ex-
tremely important for its innovation capability (Danneels, 2002).
For a firm to broaden its knowledge base, it must constantly review existing and pursue new organizational
and individual learning processes. Learning processes enable firms to transform information into new
knowledge (Crossan et al., 1999; Grant, 1996), whereby the knowledge is transferred from individuals to
groups and finally to the organizational level (Huber, 1991; March, 1991) at which the entire organization
can access the knowledge and then recombine and leverage it to generate innovation (Levinthal & March,
1993). Among other things, learning processes may be based on a continuous engagement with explorative
activities (Cohen & Levinthal, 1990; Huber, 1991) and the development and systematic training of employ-
ees (Crossan et al., 1999; Sirén et al., 2012; Sirmon et al., 2007; Thomas et al., 2001); such processes may
also be enhanced through strategic cooperation (Alvarez & Barney, 2001; Doz, 1996; Hamel, 1991). In
summary, competence plays a major role in the generation of innovation.
2.3. Cooperation
Firms are dependent on continuously expanding and diversifying their knowledge base to remain competi-
tive (Katila & Ahuja, 2002; Levinthal & March, 1993). In addition to internal measures to generate new
knowledge, the acquisition of external knowledge has also proved to be an effective strategy (e.g. Cohen
& Levinthal, 1990; Ireland et al., 2002; Mowery et al., 1996). Taking into account the resource-based view,
the specific advantage of external knowledge acquisition is the possibility of acquiring and internalizing
complementary knowledge, thereby broadening the acquirer’s own knowledge base (Das & Teng, 2000;
Tsang, 2000). Compared to internal measures, the internal generation of knowledge from adjacent fields
would often be difficult and costly to implement, because knowledge would have to be accumulated from
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scratch (Danneels, 2002; Lee & Allen, 1982). Common sources for the acquisition of external knowledge
are related firms (e.g. parent firms or sister firms), unrelated firms (e.g. suppliers or competitors), regional
networks, and research institutes (e.g. Cohen & Levinthal, 1990; Hippel, 1988).
Firms seeking to transfer and internalize external knowledge have various strategic options along transac-
tion costs economics’ continuum between market and hierarchy (Parkhe, 1993; Williamson, 1991). Market
transactions (e.g., licensing agreements) are less suitable for knowledge transfer, because the transfer of
tacit knowledge can only be secured through its continuous application (Grant, 1996), demanding close
collaboration and continuous interaction between the cooperation partners. Hierarchical structures (e.g.
mergers and acquisitions) are also only suitable in the flexible and temporary transfer of knowledge to a
limited extent, owing to their high level of integration and the associated liabilities and risks (Grant, 1996;
Harrison et al., 2001). Therefore, hybrid structures (e.g. strategic cooperation agreements) are the most
often applied governance structure for the temporary transfer of external knowledge, as they combine the
required integration level from hierarchies with the flexibility of market transactions (Hagedoorn &
Duysters, 2002; Madhok & Tallman, 1998; Williamson, 1991).
The above-mentioned characteristics have ensured that strategic cooperation has become increasingly pop-
ular in practice (e.g. Dyer et al., 2001; Gulati, 1998); moreover, the effectiveness of such strategic cooper-
ation agreements has also been illustrated in various empirical studies (e.g. Mowery et al., 1996; Parkhe,
1993; Simonin, 1997; Stuart, 2000). Accordingly, strategic cooperation agreements often play an essential
role in the innovation management of firms, as they contribute to innovation efforts in several ways, in-
cluding economies of scale, effective management of risk, cost efficient access to new markets and tech-
nologies, and learning from partners (Alvarez & Barney, 2001; Ireland et al., 2002). This effectiveness of
external knowledge in the innovation processes of corporations is also confirmed by the research field of
open innovation (e.g. Lichtenthaler, 2011; Randhawa et al., 2016; West & Bogers, 2014). Following the
above arguments and the current state of literature, cooperation is a recognized strategy to acquire external
knowledge to broaden a firm’s internal knowledge base and, thus, to generate innovation.
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2.4. The 3Cs model of innovation performance
The above-mentioned consideration of the concepts of continuity, competence, and cooperation reveals the
high dependency among them. Continuity is basically defined as the existence of defined processes and
procedures and their repeatable execution to generate innovations on a continuous basis and, thus, have a
positive effect on the innovation performance of a corporation. Various academic studies suggest that the
effectiveness of these processes can be further enhanced if the corporation has a corresponding knowledge
base (competence) to incorporate this knowledge into the innovation processes (e.g. Jantunen, 2005; Zhou
& Li, 2012). In addition, additional academic studies suggest that, in addition to the internal knowledge
base, new—often external (cooperation)—knowledge is also required for the successful generation of in-
novations (e.g. Cassiman & Veugelers, 2006; Chesbrough et al., 2006; Ebersberger et al., 2012). While the
causal relationships between the individual concepts may appear rather clear, there are only few academic
studies which examine the interplay of the concepts and confirm their positive impact on innovation per-
formance. Consequently, each concept has a positive effect on innovation performance, even though the
impact of different combinations of the concepts on the innovation performance has not yet been suffi-
ciently researched.
Figure 1: The 3Cs model of innovation performance
In order to appreciate the concepts and their interdependence, we combined them in a 3Cs model of inno-
vation performance (see Figure 1); we utilize this model to assess the impact of the elements on innovation
performance. Due to the complexity of the individual concepts, the researchers do not expect one dimension
of the model to be necessary and sufficient to explain the entire innovation performance of a firm. Instead,
the 3Cs model reveals the combinations of the concepts that lead to the innovation outcome. Therefore, this
study makes the following proposition:
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Proposition: Variations of continuity, competence, and cooperation explain a
corporation’s short-, mid-, and long-term innovation performance.
3. STUDY DESIGN
3.1. fsQCA as emerging research method
This study reports the short- and long-term observation of the interplay of continuity, competence, and
cooperation and their combined effect on a firm’s innovation performance, which is facilitated by the adop-
tion of fuzzy-set qualitative comparative analysis (fsQCA). fsQCA is a configurative research method,
which is increasingly applied in the context of business and management research and has already been
published in top-tier journals (Berger, 2016; Fiss, 2011; Kraus et al., 2018). This growing popularity is not
at least due to the fact that fsQCA can reveal complex patterns and non-linear relationships for both a small
and large number of cases (Cooper & Glaesser, 2011; Ragin, 2000). Instead of breaking cases down into a
series of independent variables, fsQCA considers them as combinations of attributes—that is, conditions
manifested by their set memberships. Therefore, fsQCA can provide a comprehensive understanding of
how various conditions combine to produce a particular outcome, while accommodating high levels of
causal complexity and identifying necessary and sufficient conditions (Ragin, 2008).
Further, fsQCA has its roots in qualitative comparative analysis (QCA) (Ragin, 2000). QCA is basically a
method that examines set-subset relationships between an outcome and all logically possible configurations
of conditions. Binary values are assigned to the outcome and the conditions to indicate their presence or
absence. Therefore, all relevant antecedents are combined in a truth table (Ordanini et al., 2014). QCA then
performs a systematic cross-case analysis on these values, which models relationships between the variables
and then uses Boolean algebra to identify configurations that have necessary and sufficient conditions for
the outcome (Ordanini et al., 2014). This process of logical reduction reduces sufficient configurations that
are redundant and thus leads to the final result of the analysis. While QCA only enables binary values fuzzy-
set QCA, fsQCA as an extension of QCA also enables the use of dichotomous values (Ragin, 2009).
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The analysis of causal relationships with less emphasis on correlations and more emphasis on set-theoretical
relations offers numerous advantages for the analysis of complex and dynamic innovation processes (Ber-
ger, 2016; Kraus et al., 2018): First, fsQCA avoids the simplistic assumption of causal symmetry that is
implied by correlation-based statistical analysis. While correlations are inherently symmetric, fsQCA al-
lows the sets of causal conditions to differ (Ragin, 2008; Woodside, 2013); second, fsQCA contributes to
equifinality, thereby ensuring that several causal paths lead to the same result (Mendel & Korjani, 2013)
and doing justice to the complexity of innovation phenomena. Due to the advantages mentioned, fsQCA is
the method of choice to handle the complexity of this study’s research question.
3.2. Data, variables, and calibration
The first step in the application of fsQCA is the preparation of an appropriated data set and the definition
of relevant variables. This is followed by the calibration of the variables, which implies the transformation
of dichotomous variables into fuzzy membership scores (Ragin, 2000, 2008).
3.2.1. Preparation of the data set
Observing the innovation behaviour of firms over a long period of time requires a corresponding data set.
Therefore, this study utilizes the Mannheim Innovation Panel, which is the German part of the Community
Innovation Survey (CIS). The Mannheim Innovation Panel is a long-term data panel based on an annual
survey on the innovation behaviour of German firms (e.g. Rammer et al., 2012). Apart from general ques-
tions on firms’ innovation behaviour, the survey incorporates an additional focus on specific innovation
topics each year. The panels for the years 2011, 2013, and 2015 have, in addition to a focus on continuity
and competence, a focus on cooperation among established firms, thereby making these data panels partic-
ularly suitable for our analysis (Rammer et al., 2012, 2014; Rammer et al., 2016).
In total, 5751 firms featured in the panel in 2011, 6208 in 2013, and 5445 in 2015 (see Table 1). Given that
the aim of this study is to conduct a longitudinal analysis, we combined the single years into a multi-year
data set. In order to ensure the quality of the analysis, we cleaned the data set by excluding firms that did
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not participate in all three years (2011, 2013, and 2015). This led to a sample of 1,463 firms. In addition,
we further cleaned the data set by excluding all cases that were not fully observed and by excluding incom-
plete cases. This resulted in a final sample of 220 firm observations, which is indicated by prior studies to
be a suitable sample for an fsQCA analysis (Berger, 2016; Kraus et al., 2018).
Table 1: Sample size of the data set
In order to facilitate the long-term perspective of the current study, we divided the analysis into three sub-
analyses—one analysis for each period under observation (see Figure 1). In the short-term analysis, we
analysed the data from the 2011 panel that encompasses the period from 2008 to 2010. The mid-term anal-
ysis also mainly uses the data from the 2011 panel but combines them with innovation performance meas-
ured in the 2013 panel (observation period 2010–2012). In the long-term analysis, we again mainly employ
the data from 2011 but combine them with the innovation performance from the 2013 and 2015 panels
(observation period 2012–2014).
Figure 2: Periods of analysis
3.2.2. Definition of variables
An fsQCA analysis examines the relationship of several causal conditions with a defined outcome (Ragin,
2000); accordingly, we define the outcome and the conditions in the following paragraphs.
Outcome: In order to observe the impact of continuity, competence, and cooperation on the innovation
performance of a firm, the outcome of the fsQCA is defined as the innovation performance. Based on our
data panel, we chose to measure innovation performance based on the revenue accruing from new products
and services. In this manner, the study focuses not only on the generation of innovation but also on market
relevance and the associated revenue. Therefore, in line with current research (e.g. Tavassoli & Bengtsson,
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2018), we chose a measure from the panel—the proportion of total turnover derived from new or clearly
improved products within the last two years.
Conditions: We selected five causal conditions2 to analyse the effects of continuity, competence, and coop-
eration on the innovation performance of a firm. To ensure a high degree of comparability with prior stud-
ies, the definition of our conditions is aligned with the literature (e.g. Cassiman & Veugelers, 2002; Ganter
& Hecker, 2014; Grimpe & Sofka, 2009).
Continuity: To measure continuity, we use the condition Continuous R&D. This condition measures
the continuous practice of R&D activities of a firm. Similar approaches to measure continuity have
already been used in current literature (e.g. Hecker & Ganter, 2016). In addition, the innovation
performance of previous periods is also included as a condition in the mid- and long-term analyses
that reflects prior innovation performance.
Competence: Competence is measured by the conditions Degrees and Training. Degrees measures
the proportion of all employees within a firm who have a university degree or other higher educa-
tion qualification. Training, on the other hand, measures the proportion of total personnel expendi-
ture used on innovation-related training. As education is appropriated to measure competence, both
conditions are suitable for our analysis and are in line with current research (e.g. Grimpe & Sofka,
2009; Tavassoli & Bengtsson, 2018).
Cooperation: To measure cooperation, we use the conditions Close cooperation and Distant coop-
eration. The condition Close cooperation indicates whether cooperation occurs with other firms
within the same corporate group or with related firms (e.g. among subsidiaries), while Distant co-
operation refers to cooperation agreements with unrelated partners (e.g. customers, suppliers, com-
petitors, consultancies, and universities). As these measures of CIS were also successfully used in
prior studies, we are in line with current research (e.g. Cricelli et al., 2016; Poot et al., 2009).
2 For a detailed mapping of the measures from the Mannheim Innovation Panel to our conditions, see Appendix 1.
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3.2.3. Calibration of variables
As already mentioned, fsQCA allows—unlike QCA – the use of dichotomous variables. This enables par-
tially ascribing membership of cases to a particular set. Therefore, it is necessary to transform variables into
fuzzy membership scores within a calibration procedure (Ragin, 2008, 2009). In particular, in the context
of qualitative data (e.g. interviews), theoretical and substantive knowledge is required to assign the individ-
ual variables to full membership (1.0), full non-membership (0.0), or partial membership (between 0.0 and
1.0). Thus, the value 0.5 represents the maximum ambiguity between membership and non-membership.
Accordingly, as we utilize already quantitative data, we transformed and calibrated our data by normaliza-
tion—we subtracted the minimum data and divided the outcome by the difference between the maximum
and minimum data (Ganter & Hecker, 2014). The process yields membership scores ranging from 0.0 to
1.0 (see Table 2).
Table 2: Descriptive statistics (not calibrated) and calibration criteria
3.3. Truth table and logical reduction
After data preparation, the actual analysis can be started. For this purpose we have used the software fsQCA
3.0, which found already application in numerous other academic studies (e.g. Del Sarto et al., 2020; Muñoz
& Kimmitt, 2019; Santos & Gonçalves, 2019; Stroe et al., 2018). While in the first step the truth table is
constructed, the evaluation of consistency and the logical reduction is conducted in a second step, which
leads to the final result of the fsQCA.
3.3.1. Construction of truth table
The construction of the truth table is an essential step in the fsQCA. The truth table represents the basic
solution space and must include all theoretically relevant configurations (Ordanini et al., 2014). In our short-
term analysis, we examined the effects of five conditions on an outcome. This resulted in a truth table with
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32 rows of configurations, of which 9 configurations (28%) were ascribed a case and 23 configurations
(72%) were not. For our mid-term analysis, we added another condition so that the truth table contains 64
configurations. Of these, 13 configurations (20%) were ascribed a case and 51 configurations (80%) were
not. The long-term analysis was conducted with seven conditions. Of the possible 128 configurations, 19
configurations (15%) were ascribed a case and 109 configurations (85%) were not.
3.3.2. Logical reduction
The next step within the fsQCA approach is to define a frequency and a consistency threshold (Ragin,
2008). The definition of a frequency threshold is used to classify which specific configurations are consid-
ered empirically relevant. In our analysis we set a frequency threshold of 1. This implies that every config-
uration with one or more cases is included in our analysis. Based on our data, a higher frequency threshold
was not considered, since the assignment of cases to configurations shows a long tail and would, therefore,
have led to the exclusion of an excessively large number of relevant configurations. The consistency de-
scribes if membership scores in a particular causal set are less than or equal to the membership scores in
the outcome set. The consistency can assume values between 0.0 and 1.0, where values below 0.75 indicate
inconsistencies (Ragin, 2009). Thus, consistency is comparable with significance in statistical procedures.
Following the recommendations in academic literature and to ensure empirical evidence, we set a con-
sistency threshold of 0.8 for our analysis (Ragin, 2008).
The last step of the fsQCA is the logical reduction of sufficient configurations. Thereafter, the coverage for
the final configurations is calculated. Coverage indicates the extent to which a configuration is responsible
for a high score of the outcome set (Ragin, 2008). The coverage can assume values of between 0.0 and 1.0
and is comparable to the variance (R-Square) in statistical methods. Within our fsQCA, the logical reduc-
tion led to three configurations for the short-term analysis, one configuration for the mid-term analysis, and
five configurations for the long-term analysis. The individual configurations are considered in the next
section.
17
4. RESEARCH FINDINGS
The result of the fsQCA analysis is presented in Table 3. Within the solution table, filled circles indicate
the presence of a condition and empty circles indicate its absence. Further, the size of the circles indicates
whether the condition acts as a core or as a peripheral condition. This classification, introduced by Fiss
(2011), makes it possible to differentiate the conditions according to the strength of evidence in relation to
the outcome. While core conditions (large circles) occur in the parsimonious and intermediate solution and
are, therefore, central to the solution, peripheral conditions (small circles) appear only in the intermediate
solution (Fiss, 2011). In addition to the presence and absence of a condition, dashes indicate that the pres-
ence or absence of a particular condition is not important to a particular configuration.
Table 3: Configurations explaining innovation performance
In order to address the requirements of a longitudinal study, we have grouped similar configurations and
presented them on a timeline.
Figure 3: Mapping of configurations to timeline
4.1. Short-term analysis
With regard to the outcome Innovation performance 2010, three configurations are observed. The solution
set displays a satisfactory overall consistency level of 0.733 and a coverage level of 0.646.
Configuration(s) S-C1Firms with a focus on (distant) cooperation supported by continuous
R&D: The configuration states that 87% of all firms with the characteristics of Distant cooperation
(core condition), Close cooperation, and Continuous R&D (both peripheral conditions) between
2008 and 2010 were able to generate marketable innovation in 2010. The conditions Training and
18
Degrees were not relevant within S-C1 to explain the outcome, as both conditions were absent as
peripheral conditions.
Configuration(s)—S-C2, S-C3Firms with a focus on continuous R&D and avoidance of (close)
cooperation: The configurations are rather similar in that both feature the presence of the core
condition Continuous R&D and the absence of the conditions Close cooperation (a core condition)
and Distant cooperation (a peripheral condition). In addition, the consistency between the two con-
figurations is rather similar, as S-C2 reveals that 74% of all firms that practiced continuous R&D
while omitting cooperation between 2008 and 2010 were able to generate marketable innovation in
2010 and S-C3 reveals that this applies to 77% of the firms. However, it must be noted that S-C2
falls below the generally accepted minimum consistency of 0.75 and must, therefore, be treated
with extreme caution in further analysis (Ragin, 2008). An overarching perspective also indicates
that the conditions Training and Degrees are of only minor relevance for the outcome, as in all
configurations, these conditions only appear as a peripheral condition and are either absent or have
a neutral permutation.
4.2. Mid-term analysis
Considering Innovation performance 2012 as an outcome and adding Innovation output 2012 as a further
condition to our prior set of conditions, only one configuration emerged. The solution set displays an overall
consistency level of 0.825 and a coverage level of 0.646.
Configuration(s) M-C1—Firms with high levels of workforce education and prior innovation track
record: The configuration indicates the presence of the core conditions Innovation performance
2010 and Degrees. Conditions Close cooperation, Distant cooperation, and Training are absent as
peripheral conditions, while Continuous R&D indicates a neutral permutation. Taking this result
into account, the configuration states that 82% of all firms that generate successful innovation in
2010 and have maintained a high level of workforce education—measured by the proportion of all
19
employees who have a university degree or other higher education qualification—in at least the
years 2008–2010 were able to generate marketable innovation performance in 2012. The configu-
ration does not indicate any similarities with the configurations from the short-term analysis.
4.3. Long-term analysis
For the outcome Innovation performance 2014, we observe five configurations. The solution set displays
an overall consistency level of 0.739 and a coverage level of 0.728.
Configuration(s) L-C1—Firms with high levels of workforce education and prior innovation track
record: This configuration is more or less similar to the M-C1 from the mid-term analysis. The
configuration states that 86% of all firms with the characteristics of Innovation performance 2012
and Degrees (both core conditions) between 2008 and 2010 were able to generate marketable in-
novation in 2014, while the other conditions are absent as peripheral conditions or are neutrally
permutated. When compared to M-C1, it is evident that innovation performance 2012 has a neutral
permutation. This observation is reviewed in detail in the discussion section.
Configuration(s) L-C2, L-C3, and L-C4—Firms with a focus on continuous R&D and avoidance
of (distant) cooperation: Each of the three configurations indicates the presence of the core condi-
tion Continuous R&D and a simultaneous absence of the core condition Distant cooperation. All
other conditions are absent as peripheral conditions or are neutrally permutated. Given this hetero-
geneity, it is not possible to extend the generalizability. Therefore, each of the three configurations
(L-C2: 82%; L-C3: 70%; L-C4: 100%) strongly reveals that firms practicing continuous R&D and
avoiding distant cooperation between 2008 and 2010 were able to generate marketable innovation
in 2014. However, L-C3 falls below the generally accepted minimum consistency of 0.75 and must
therefore be treated with extreme caution in further analysis (Ragin, 2008).
Configuration(s) L-C4, L-C5 – Firms with a continuous innovation track record: L-C4 and L-C5
are similar in terms of the presence of the core condition Innovation performance 2012 and of the
20
peripheral condition Innovation performance 2010. In addition, L-C5 does not show any conditions
that permit further conclusions, as all other conditions are absent as peripheral conditions or are of
a neutral permutation. Therefore, L-C5 states that 91% of all firms with innovation performance in
2010 and 2012 were able to generate marketable innovation in 2014. In contrast, L-C4 also indicates
the presence of the core condition Continuous R&D, while the core condition Distant cooperation
is absent; therefore, it is partially similar to L-C2 and L-C3. L-C4 states that 100% of all firms with
high innovation performance in 2010 and 2012 and those practicing continuous R&D and avoiding
distant cooperation between 2008 and 2010 were able to generate marketable innovation in 2014.
5. DISCUSSION
Based on our analysis, similar configurations were grouped and summarized as per of the 3Cs Model (see
Figure 4). This exercise reveals that continuity—in the form of continuous R&D activities and in the form
of prior innovation performance—is of utmost importance, as it is included in all configurations in each
observed period. It must be noted that although continuity appears in certain configurations in interaction
with competence (M-C1, L-C1) and cooperation (S-C1), it can also be solely responsible for short-term (S-
C1, S-C2) and long-term innovation performance (L-C2–L-C5). This observation illustrates the great im-
portance of continuous R&D and prior innovation performance and is in line with current research, as pre-
vious studies have also examined their considerable importance (e.g. Boer & Gertsen, 2003; Hargadon,
1998; Karlsson & Björk, 2017; Steiber & Alänge, 2013; Xu et al., 2010). Moreover, this observation also
suggests that although competence and cooperation can be important elements in the generation of innova-
tions, they play only a subordinate role when compared to the continuous prosecution of R&D. Therefore,
continuity is an essential prerequisite for the successful generation of innovations, whether in the short-,
mid-, or long-term.
Figure 4: Mapping of configurations to the 3Cs model
21
Further, competence turns out to be important for the mid- and long-term generation of innovation. How-
ever, in our analysis, competence does not occur in isolation in any observed configuration, but in both
configurations exclusively in combination with continuity (M-C1 and L-C1). Therefore, the preliminary
conclusion is that the continuous prosecution of R&D supports the development of internal competencies
and enables a firm to generate successful innovations. This finding supports current literature, as the devel-
opment of competence and related knowledge is only possible if pursued continuously (Crossan et al., 1999;
Grant, 1996; Jantunen, 2005; Zhou & Li, 2012). Further, the continuous practice of R&D is a core activity
required to expand a firm’s knowledge base and therefore helps to avoid a competence trap (Dougherty,
1995; March, 1991; Sirén et al., 2012). However, considering the lead times observed in our analysis, it
takes at least two years until the combination of continuity and competence leads to successful innovation.
A further observation is associated with the measures used for the operationalization of competence: Train-
ing and Degrees. However, it is striking that Training, even if related to innovation, has no relevance for
innovation performance. In all cases where competence is represented, this is due to the proportion of em-
ployees with high-level educational qualifications. This leads us to question the role of innovation training
to build respective competence; instead, competence appears to be based on a high degree of pre-existing
knowledge in the form of educated employees. This may be due to the fact that higher-educated employees
already have the skills to acquire and process new relevant knowledge independently and are, therefore,
more successful in terms of generating innovation (Koellinger, 2008; Romero & Martínez-Román, 2012).
Therefore, an appropriate level of highly educated employees who participate in continuous R&D activities
are likely to support mid- to long-term innovation performance.
Cooperation particularly affects short-term innovation performance. However, the corresponding configu-
ration—similar to competence—does not occur in isolation but only together with continuity (S-C1). Here,
too, we conclude that only cooperation agreements that operate simultaneously with the continuous prose-
cution of R&D will lead to success. This can be explained by the fact that for the successful development
and marketing of innovations merely acquiring external knowledge through cooperation is insufficient on
its own; it is also important to ensure the additional transfer of that knowledge into innovative technologies
22
and products through appropriate R&D measures (Ebersberger et al., 2012; Hamel, 1991; Mowery et al.,
1996). Further, several studies have found that the management of strategic cooperation agreements is as-
sociated with a high degree of complexity. However, continuous practice can produce learning effects,
which make cooperation agreements and their output more successful (Ireland et al., 2002). A closer exam-
ination of the truth table reveals differences between close cooperation and distant cooperation. While in
the short term there is a stronger focus on distant cooperation agreements, in the long term, there is a
stronger focus on close cooperation agreements. This finding reinforces our previous assumptions that in
the short-term external knowledge must be acquired (distant cooperation), while in the long-term innovation
must be generated primarily through internal knowledge (where required through close cooperation agree-
ments in addition to a firm’s own competence).
The results also clarify that there are no configurations where both competence and cooperation are present.
This might be because we have included time as an additional dimension within our analysis. Therefore,
both concepts evidently influence innovation performance, but at different points in time. Nevertheless, this
does not imply that the measures are fundamentally mutually exclusive but that competence and coopera-
tion must be regarded as complementary concepts, each with specific advantages and disadvantages.
6. IMPLICATIONS, FURTHER RESEARCH, AND LIMITATIONS
6.1. Theoretical implications
This study contributes to the innovation management literature by providing evidence of the interplay of
the concepts of continuity, competence, and cooperation and their effect on a corporation’s innovation per-
formance. First, our work confirmed that continuity, competence, and cooperation are essential concepts in
the innovation management literature. Further, our analysis indicated the importance and impact of compe-
tence and cooperation. We were able to show that cooperation has a short-term effect on the innovation
performance of corporations, while competence has a long-term effect on innovation performance. In ad-
dition, our work has enabled us to highlight the essential importance of continuity and to differentiate it
23
from competence and cooperation. While continuity has been implicitly considered in numerous prior stud-
ies, we have been able to explicitly work out its effect—continuity is an essential prerequisite for the suc-
cessful generation of innovations. In addition to the fundamental contribution to the research field of inno-
vation management, we also make a significant contribution to the research stream of continuous innovation
(e.g. Boer & Gertsen, 2003; Hargadon, 1998; Steiber & Alänge, 2013; Xu et al., 2010).
Second, with this study, we contribute to the research field of organizational learning. On the one hand, we
have ascertained that competence can contribute to long-term innovation success; however, on the other
hand, we have also observed that innovation-related training does not play a significant role in this respect.
Instead, a high proportion of employees with a university degree or other higher education qualification has
a positive effect on competence. This finding is partially contradictory to existing research. However, we
must add that the data does not provide any information regarding the particular design of the reported
trainings. Consequently, a closer examination of the specifics of different innovation-trainings appears nec-
essary.
Third, by utilizing fsQCA and considering the results of our analysis, we were able to illustrate the suita-
bility of configurational methods for answering complex questions in the context of innovation manage-
ment. Further, we were able to analyse the complex interactions among different concepts for a large num-
ber of cases (n = 220) and determine their effect on a predefined outcome. In the context of innovation
management, researchers must therefore include configurational methods in their methodological toolkit
and apply them to corresponding research questions.
6.2. Managerial implications
The findings of this study also have managerial implications. Managers must be aware of the importance
of implementing innovation processes within the organization and practice them on a continuous basis. This
is particularly true in times of an economic downturn, when it can often be observed that numerous corpo-
rations discontinue their innovation activities and restart them when the economy begins to recover (Gaba
24
& Dokko, 2016; Knyphausen-Aufseß, 2005). This on-off-mentality is always associated with correspond-
ing lead times, so that initiatives can almost never reach their full potential. Therefore, the sustainable de-
velopment of innovations is not a sprint, but a marathon. In order to support the short-term development of
innovations, managers must examine the opportunities and possibilities of innovation cooperation . How-
ever, it is important to bear in mind that although cooperation can make a short-term contribution to inno-
vation success, it does not have a sustainable and long-term effect. For the long-term and sustainable de-
velopment of innovations, managers must focus on competence within the corporation.
6.3. Further research and limitations
Despite the determination of the short-, mid-, and long-term effects of continuity, competence, and cooper-
ation on the innovation performance of a firm, there are limitations to this study that pave the way for
potentially interesting future research. First, the time intervals of this longitudinal study were determined
by the availability of data in the Mannheim Innovation Panel rather than being based on concrete theoretical
reasons. Although the data that we analysed did make it possible to differentiate between short-, mid-, and
long-term effects and utilizing the three different time intervals mitigates this issue, future research could
possibly follow research designs with time intervals that are clearly grounded on theoretical assumptions.
Second, the configurational analysis does justice to the complexity of the phenomenon, but it is not possible
to relate the results based on the Mannheim Innovation Panel to specific firms. Hence, it is not possible to
illustrate certain configurations with concrete cases that might also be informative and create a deeper un-
derstanding of the underlying mechanisms. Therefore, future research could investigate case studies that
are based on the suggested configurations. Third, contrary to linear analyses, it is rather unusual for config-
urational methods to employ control variables in the analysis. The inclusion of additional conditions
changes the logically possible (and thus the empirically observed) configurations of conditions (Greck-
hamer et al., 2018) and potentially adds a substantial amount of complexity to the results, thereby causing
researchers to run the danger of producing incomprehensible and meaningless results. Instead, configura-
tional methods follow an effect-of-causes model, where conditions are included only if they are considered
25
to be among the major causes of the outcome. Nevertheless, studies that analyse continuity, competence,
and cooperation with the help of linear methods that include control variables and investigate the interaction
among the core concepts promise to be a fruitful endeavour.
7. CONCLUSION
The generation of innovation plays a major role in the sustainable success of corporations—particularly in
times that are characterized by increasing market and technological dynamics. Therefore, it is all the more
important for corporations to focus on continuous R&D and to build up corresponding competencies and
cooperation. Although prior innovation research on these topics is rather informative and valuable, only a
little research focuses on the complexity and interdependence of the concepts of continuity, competence,
and cooperation. Using a configurational research approach, the present work addresses this gap in the
academic literature. Considering the topics of continuity, competence, and cooperation, we observed dif-
ferent configurations and analysed how they interact with each other and how they affect the innovation
performance of a firm on a short-, mid-, and long-term basis. We found that continuity is the basic require-
ment for constant innovation performance. In addition, cooperation is likely to be supportive of innovation
performance in the short-term, while competence supports innovation performance in the long-term.
26
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Table 1: Sample size of the data set
Panel Total sample Sample after consolidation of years Final sample after data cleaning
2011 5751
1463 220
2013 6208
2015 5445
44
Table 2: Descriptive statistics (not calibrated) and calibration criteria
Variable
Variable Descriptive statistics (not calibrated) Calibration criteria
Values Min. Max. Mean Std. Dev.
Non-
membe
r
Cross-
ove
r
Full-
membe
r
Innovation performance 2014 Ordinal (0 to 8) 0.000 8.000 1.209 2.139 0.000 4.000 8.000
Innovation performance 2012 Ordinal (0 to 8) 0.000 8.000 0.973 1.979 0.000 4.000 8.000
Innovation performance 2010 Ordinal (0 to 8) 0.000 8.000 1.118 2.197 0.000 4.000 8.000
Close cooperation Interval (0 to 2) 0.000 2.000 0.064 0.280 0.000 1.000 2.000
Distant cooperation Interval (0 to 12/16)3 0.000 12.000 0.645 1.428 0.000 6.000 12.000
Continuous R&D Binary (0, 1) 0.000 1.000 0.182 0.387 0.000 0.500 1.000
Training Ordinal (0 to 0.1) 0.000 0.010 0.012 0.016 0.000 0.005 0.010
Degrees Ordinal (0 to 8) 0.000 8.000 3.382 2.466 0.000 0.400 8.000
3 For the data set of 2011, there are 12 binary cooperation measures that we combined to create an interval from 0 to 12. For the data sets of 2013 and 2015,
the cooperation measures were extended to 16 binary measures, which were combined by us in an interval from 0 to 16. For more details, see Appendix 1.
45
Table 3: Configurations explaining innovation performance
Configuration/Conditions Short-term
analysis Mid-term
analysis Long-term
analysis
-C1
-C2
-C3
M
-C1 L-C1 L-C2 L-C3 L-C4 L-C5
Continuity
Innovation performance 2012 n/a n/a n/a n/a - -
Innovation performance 2010 n/a n/a n/a -
Continuous R&D - -
-
Competence
Training - -
Degrees - - -
Cooperation
Close cooperation -
Distant cooperation
Raw coverage 0.080 0.600 0.351 0.646 0.545 0.240 0.523 0.268 0.545
Unique coverage 0.045 0.250 0.001 0.646 0.014 0.108 0.056 0.030 0.015
Consistency 0.873 0.743 0.771 0.825 0.864 0.824 0.699 1.000 0.912
Overall solution coverage 0.646 0.646 0.728
Overall solution consistency 0.733 0.825 0.739
46
Core condition
(present)
Core condition
(absent) -
Neutral permutation—that is,
presence or absence does not
matter
Peripheral condition
(present) Peripheral condition
(absent) n/a The condition within this solu-
tion is not available
47
Figure 1: The 3Cs model of innovation performance
48
Figure 2: Periods of analysis
49
Figure 3: Mapping of configurations to timeline
50
Figure 4: Mapping of configurations to the 3Cs Model
51
Appendix 1
Constructed Variables Mannheim Innovation Panel
Variable Year Values Name Data set Description Values
Innovation
performance
2011
2013
2015
Ordinal
(0–8)
umneu 2011
2013
2015
Proportion of total turnover from new or
clearly improved products
Ordinal
(0 to 8)
Close
cooperation
2011
2013
2015
Interval
(0–2)
kod1 2011
2013
2015
Cooperation with other firms within the same
group of companies or related companies in
Germany
Binary
(0 = no, 1 = yes)
koa1 2011
2013
2015
Cooperation with other firms within the same
group of companies or related companies in
abroad
Binary
(0 = no, 1 = yes)
Distant
cooperation
2011 Interval
(0–12)
kod2 2011 Cooperation with clients in Germany Binary
(0 = no, 1 = yes)
kod3 2011 Cooperation with suppliers in Germany Binary
(0 = no, 1 = yes)
kod4 2011 Cooperation with competitors or other firms
in the same sector in Germany
Binary
(0 = no, 1 = yes)
kod5 2011 Cooperation with consultancy firms and pri-
vate R&D firms in Germany
Binary
(0 = no, 1 = yes)
kod6 2011 Cooperation with universities and other
hi
g
her education institutions in German
y
Binary
(
0 = no, 1 =
y
es
)
kod7 2011 Cooperation with public and non-profit-mak-
ing private research institutions in Germany
Binary
(0 = no, 1 = yes)
koa2 2011 Cooperation with clients abroad Binary
(0 = no, 1 = yes)
koa3 2011 Cooperation with suppliers abroad Binary
(0 = no, 1 = yes)
52
koa4 2011 Cooperation with competitors or other firms
in the same sector abroa
d
Binary
(0 = no, 1 = yes)
koa5 2011 Cooperation with consultancy firms and pri-
vate R&D firms abroad
Binary
(
0 = no, 1 =
y
es
)
koa6 2011 Cooperation with universities and other
higher education institutions abroa
d
Binary
(0 = no, 1 = yes)
koa7 2011 Cooperation with public and non-profit-mak-
ing private research institutions abroa
d
Binary
(0 = no, 1 = yes)
2013
2015
Interval
(0–16)
kod2 2013
2015
Cooperation with customers from the private
sector and private households (Germany,
abroad)
Binary
(0 = no, 1 = yes)
kod3 2013
2015
Cooperation with customers from the public
sector in Germany
Binary
(0 = no, 1 = yes)
kod4 2013
2015
Cooperation with suppliers in Germany Binary
(0 = no, 1 = yes)
kod5 2013
2015
Cooperation with competitors or other firms
in the same sector in Germany
Binary
(0 = no, 1 = yes)
kod6 2013
2015
Cooperation with Consultants or consulting
engineers in Germany
Binary
(0 = no, 1 = yes)
kod7 2013
2015
Cooperation with universities or universities
of applied sciences in Germany
Binary
(0 = no, 1 = yes)
kod8 2013
2015
Cooperation with public research institutions
in Germany
Binary
(0 = no, 1 = yes)
kod9 2013
2015
Cooperation with private research institutions
in Germany
Binary
(0 = no, 1 = yes)
koa2 2013
2015
Cooperation with customers from the private
sector and private households abroad
Binary
(0 = no, 1 = yes)
koa3 2013
2015
Cooperation with customers from the public
sector abroa
d
Binary
(0 = no, 1 = yes)
53
koa4 2013
2015
Cooperation with suppliers abroad Binary
(0 = no, 1 = yes)
koa5 2013
2015
Cooperation with competitors or other firms
in the same sector abroa
d
Binary
(
0 = no, 1 =
y
es
)
koa6 2013
2015
Cooperation with consultants or consulting
engineers abroa
d
Binary
(0 = no, 1 = yes)
koa7 2013
2015
Cooperation with universities or universities
of applied sciences abroad
Binary
(0 = no, 1 = yes)
koa8 2013
2015
Cooperation with public research institutions
abroad
Binary
(0 = no, 1 = yes)
koa9 2013
2015
Cooperation with private research institutions
abroad
Binary
(0 = no, 1 = yes)
Continuous
R&D
2011
2013
2015
Binary
(0 = R&D never
or occasionally
conducted, 1 =
continuous
R&D)
fuekon 2011
2013
2015
Continuous R&D activities 0 = R&D never
conducted, 1 =
continuous
R&D, 2 = occa-
sional R&D
Training 2011
2013
2015
Ordinal
(0–0.1)
wbp 2011
2013
2015
Proportion of total personnel expenditure used
for further training
Ordinal
(0–0.1)
Degrees 2011
2013
2015
Ordinal
(0–8)
bhsp 2011
2013
2015
Proportion of all employees who have a uni-
versity degree or other higher education quali-
fication
Ordinal
(0–8)
Chapter
The objective of this study is to determine the effect of R&D cooperation and non-R&D cooperation in the introduction of incremental and radical innovations. R&D cooperation tackles engineering, design, intellectual property, software development and database, and acquisition of tangible assets. Non-R&D cooperation is related to financing, training, and information. Using the National Survey of Innovation Activities of Ecuador 2009–2014, Tobit models are estimated to determine the effects of R&D and non-R&D cooperation on sales of non-innovative products, incremental innovations, and radical innovations are estimated. The results show that R&D cooperation boosts a transition from selling non-innovative products to selling incremental and radical innovations. This happens by increasing the percentage of new products, for both firm and market, and decreasing the percentage of sales of non-innovative products. In addition, cooperation in other aspects other than R&D increases incremental innovations, rather than radical ones. The R&D expenditure boosts incremental and radical innovations but reduces sales in non-improved products. When a firm is part of a corporative group, its sales in radical innovations are higher in 3.3% points than firms that are not part of a corporative group. the composition of labor in a firm is a key aspect for radical, incremental, and non-innovative products. Our results show that graduated workers boost radical innovations, more than undergraduate workers. By contrast, an increase of graduated workers reduces the sales of non-improved products, constituting an expenditure, rather than an investment for firms that keep selling non innovative products.
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This study conducts an empirical analysis on the relationship between innovation and the type of partner based on the assumption that the knowledge and information acquired from partners would vary depending on their type from the perspective of learning through technology cooperation. It further expands the discussion by looking at the relationship between geographic distance between partners and innovation as well as absorptive capacity, a variable that moderates it. The knowledge required for product development is classified into explicit and implicit knowledge, and based on such knowledge type, the form of learning and innovation is categorized into STI (Science, Technology and Innovation) and DUI (Doing, Using and Interacting). Accordingly, technology cooperation partners are divided into STI and DUI partners. The study analyzes the effect of the cooperation partner type on radical and incremental innovation. Unlike the hypothesis, cooperation with a STI partner had a positive effect on incremental innovation while a DUI partner had such effect on radical innovation. The geographical distance between partners had a negative effect on incremental innovation and the moderating effect of appropriability was not verified.
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Open Innovation describes an emergent model of innovation in which firms draw on research and development that may lie outside their own boundaries. In some cases, such as open source software, this research and development can take place in a non-proprietary manner. Henry Chesbrough and his collaborators investigate this phenomenon, linking the practice of innovation to the established body of innovation research, showing what's new and what's familiar in the process. Offering theoretical explanations for the use (and limits) of open innovation, the book examines the applicability of the concept, implications for the boundaries of firms, the potential of open innovation to prove successful, and implications for intellectual property policies and practices. The book will be key reading for academics, researchers, and graduate students of innovation and technology management.
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