The Future of the Management of Innovation: Trends and Challenges

Full text

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The Future of the Management of Innovation: Trends
and Challenges
Achim Hecker & Franz Huber
Seeburg Castle University
Forthcoming in: Tang, M. and C. H. Werner, Eds. (2017):
Handbook of the Management of Creativity and Innovation:
Theory and Practice. World Scientific
1. Introduction
The contributions in this book illustrate that innovation management encompasses
a variety of dimensions and fields of application. There is widespread agreement
among policy makers, managers and academics that understanding and managing
innovation is a key challenge for future success of businesses and economies. The
increasing speed of economic and societal change, including transformative forces
such as digitalization, creates much demand for innovation and innovation
management. Against this backdrop, it comes with no surprise that the academic
field of innovation studies, as well as the discussions on innovation management
among practitioners, are flourishing. The focus of the present chapter is to
comment on trends and future challenges of innovation management. It is worth
mentioning that commenting on the future of managing innovation on a couple of
pages involves limitations. First, the future is open and to some extent
unpredictable. Second, as innovation includes many fascinating dimensions, it is
impossible to provide a comprehensive overview. We aim to address these issues
(i) by focusing on trends which are already ongoing and can be projected with
some degree of certainty, and (ii) by focusing on topics which are related to our
own research.
In the first part of the chapter, we discuss three fundamental dimensions
for the future of innovation management: the importance of non-technological
innovation (section 2), innovation dynamics (section 3) and global systems of
innovation (section 4). The remaining sections stress substantive trends, which
will shape innovation management: managing innovation for environmental
sustainability (section 5) and digital innovation (section 6), before we conclude in
section 7.

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2. Managing Non-Technological Innovation
The study and management of innovation was, for a long time, confined to the
technological development of new products and production processes.
“Historically, research on innovation types has followed a technological
imperative (...) focused on a narrow definition of product and process innovations
associated with the R&D function in manufacturing organizations (...). Studies of
organizational or administrative innovations have been relatively scarce”
(Damanpour, Walker, & Avellaneda, 2009, p. 651). A recent survey of the past 27
years’ innovation literature finds that out of 524 articles published in leading
management journals, no more than 3% deal with innovation in administrative
processes, organizational structures and management practices (Crossan &
Apaydin, 2010).
Recently, researchers and practitioners increasingly question such a narrow
notion focusing exclusively on technological innovation. They point to the obvious
fact that innovation is not restricted to the development of new products and
production processes but also finds fertile ground in services as well as a firm’s
organizational structures, administrative processes and managerial practices
(Birkinshaw, Hamel, & Mol., 2008; Damanpour & Aravind, 2012; Hamel, 2007).
Numerous examples – such as financial services; the divisional structure; the
Tayloristic workplace organization; cost accounting and capital budgeting; Total
Quality Management or the Toyota production system – illustrate the relevance of
non-technological innovation for firm performance as well as economic growth.
New studies are now beginning to make up for this bias towards product and
process innovation by delving into the topics of antecedents, characteristics, and
consequences of non-technological innovation. Quantitative studies employing
statistical methods to determine general patterns of non-technological innovation
behavior across a large number of diverse firms are hampered, however, by a lack
of available data. In contrast to technological innovation, organizational and
service innovations are usually not patentable, which obviates patent statistics as
source of quantitative data for these innovation types. As these types do not rely
on conventional resources and processes of research and development, statistics
on R&D inputs are likewise of little relevance. Furthermore, most surveys on
firms’ innovation behavior follow the mainstream of innovation research by
focusing exclusively on technological forms of innovation, notably on the
development of new products and production processes.
This situation has somewhat improved with the integration of specific items
relating to organizational and service innovation into the Community Innovation
Survey as one of the largest innovation surveys worldwide. Recent studies have

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made use of this data to advance our understanding of determinants and effects of
organizational innovation (e.g., Battisti & Stoneman, 2010; Evangelista &
Vezzani, 2010; Ganter & Hecker, 2013a; 2013b; 2014; Hecker & Ganter, 2013;
2014; Mol & Birkinshaw, 2009; 2012; Mothe & Nguyen-Thi, 2012). These studies
sow the seeds of an emerging new field in innovation research which holds promise
to significantly contribute to a more complete picture of innovation management
that comprises technological as well as non-technological forms of innovation.
For the future of innovation management, we expect important insights on a
number of key issues on the emerging agenda of research on organizational and
service innovation, which include: (i) Investigating drivers and contingencies of
non-technological innovation at the firm, industry and country level as well as
interdependencies between factors from these levels. This also comprises detailed
studies on the impact of national institutions (e.g., product-, labor-, and financial
market regulations, industrial and labor relations, educational system, legal
regime, public (research) infrastructure, as well as national culture) in addition to
a firm’s industrial and organizational context on its non-technological innovation
conduct and performance. (ii) Determining interdependencies between factors and
processes leading to non-technological and technological innovation and deriving
insights about complementarities between various innovation types. As
interdependencies between innovation types usually unfold over time, research on
this topic will also contribute to a better understanding of complex trajectories of
innovation adoption and innovation dynamics more generally (see also the
following section). (iii) Researching the impact of organizational innovation on
firm performance. While the relevance of technological innovation for firm
success is largely uncontested, performance implications of organizational and
management innovations are still controversially disputed and lack robust
evidence.
These insights will lead practitioners of innovation management to a more
complete understanding of innovative activities within their organization. Whereas
most organizations maintain resources dedicated to developing product innovation
(e.g., research personnel, R&D labs) or process innovation (e.g., production
engineers, quality circles) and at the same time sustain institutionalized processes
for their development (e.g., stage-gate innovation processes, continuous
improvement processes), both are usually non-existent with respect to the
development of non-technological forms of innovation. Rather, particularly
organizational and management innovations are often the result of initiatives
undertaken by entrepreneurially inclined employees who depart from customary
ways of doing business, trying something new, usually without being asked or
expected to do so and sometimes even without being given permission by higher
management to do so (Hecker, 2015). They therefore fall into the realm of internal

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venturing and intrapreneurship. Analyzing them through the theoretical lens of an
augmented notion of innovation management comprising technological and non-
technological advance promises to unleash the organization’s full innovation
potential.
a. Managing Innovation Dynamics
Innovation is a dynamic phenomenon. Current innovation behavior of firms is
influenced by previous innovation decisions and determines future innovation
capacity (e.g., Damanpour et al., 2009; Flaig & Stadler, 1994). Complementarities
between different (types of) innovations unfold over time and constitute a path-
dependent sequence of innovation activities and events (Ganter & Hecker, 2013;
Hecker & Ganter, 2014). Innovation projects can, therefore, not be managed or
analyzed in isolation, but must be understood from the whole context of
concomitant activities and path-dependent innovation trajectories.
However, the study and management of innovation dynamics is still in its
infancy. Most research on innovation still focuses on single innovation events and
mainly relies on a cross-sectional set-up for empirical study. Innovation efforts in
organizations are mainly managed as insulated projects without sufficient
consideration of interdependencies and intertemporal complementarities. One
reason for this lacuna in current research and management practice may lie in the
lack of available data covering a sufficient period of time to capture intertemporal
patterns of innovation activities. Another reason is the remarkable increase in
complexity once intertemporal relations are taken into account. As the number of
possible innovation paths increases exponentially in their considered path length,
so does the number of factors influencing a single innovation decision when
allowing for intertemporal complementarities.
Nonetheless, there have been attempts to advance our knowledge of innovation
dynamics ranging from a micro-perspective (e.g., scrutinizing the genesis of a
particular innovation within an exemplary firm) to mapping the temporal diffusion
of innovations across firms, industries, and countries. With respect to sequential
and combinative patterns of innovation types, early studies focus on the dynamic
interrelation of product and process innovation and link this interrelationship to
the life cycle of technologies and markets (Abernathy & Utterback, 1978; Ansoff
& Stewart, 1967; Utterback & Abernathy, 1975). While product innovation refers
to the market introduction of a product or service that is new or significantly
improved, process innovation aims at increasing the efficiency of internal
production and delivery processes. During the early development of technologies
and markets, product innovations are key to reconcile technological options and
user demands and shape core characteristics of valuable applications. Once a

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dominant product design has emerged, the focus shifts from product definition and
differentiation to production performance and costs. As a consequence, product
innovation dominates early phases in the life cycle of a technology, whereas
process innovation is particular pertinent to later stages. As industries significantly
vary in their technological trajectories and life cycles, these considerations imply
that adoption patterns and sequences should significantly differ across industries.
More recent studies on innovation dynamics have advanced these early
attempts by additionally tracing the relationship between technological and non-
technological innovation types in a dynamic setting (Damanpour et al., 2009;
Ganter & Hecker, 2013b; Hecker & Ganter, 2014). They show optimal type
composition and adoption sequence to be path-dependent and determined by firm-
level attributes including functional differentiation, specialization, firm size, and
organizational slack. In addition, Damanpour et al. (2009) shows less of an
industry-specific innovation pattern that is beneficial to organizational
performance, but more an organization’s divergence from industry norms in
adopting innovation types. These results suggest that there is not only considerable
heterogeneity of innovation sequences across but also within industries (at least
for some sectors).
Another stream of literature has shed some light on innovation dynamics from
a different angle by researching patterns of innovation persistence (e.g., Cefis,
2003; Cefis & Orsenigo, 2001; Ganter & Hecker, 2013b; Geroski, Van Reenen &
Walters, 1997; Hecker & Ganter, 2014; Malerba & Orsenigo, 1999; Mañez,
Rochina-Barrachina, Sanchis & Sanchis, 2009; Peters, 2009; Raymond, Mohnen,
Palm, & Loeff, 2010; Roper & Hewitt-Dundas, 2008). Innovation persistence
describes a form of state-dependence in which previous innovation activity fosters
(or constrains) current innovation behavior and success, such that great (non-
)innovators have a tendency to remain in their historic state. Innovation persistence
has far-reaching ramifications for topics in innovation theory and practice,
strategic management, and public policy. At the macroeconomic level, persistence
of innovation substantiates endogenous growth models and recognizes incumbent
firms and cumulative knowledge building as important source of innovation and
economic growth. At the same time it dismisses new entrants and their ‘creative
destruction’ and therefore could represent an important argument in the
longstanding debate between the Schumpeter Mark I and Mark II models (Malerba
& Orsenigo, 1996). At the microeconomic level, a continuous loop of innovation
represents an important instance of the ‘success breeds success’ hypothesis (Flaig
& Stadler, 1994), and provides a major building block of sustained competitive
advantage and lasting interfirm performance differences. Finally, a public policy
perspective of innovation persistence underscores important lessons for designing
and targeting innovation support programs. Such persistence potentially implies

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intertemporal spillovers relevant for evaluating the impact of innovation programs.
It also casts doubt on the wisdom of subsidizing start-up firms and new market
entrants when innovation promotion is the primary funding goal.
Over the recent years, a number of studies have investigated persistence in
innovative activities across different countries (e.g., Cefis & Orsenigo, 2001;
Malerba & Orsenigo, 1999), for different industries (e.g., Cefis & Orsenigo, 2001;
Raymond et al., 2010) including the service sector (e.g., Peters, 2009), for
innovation inputs (e.g., Peters, 2009; Mañez et al., 2009) and outputs (e.g., Cefis,
2003; Geroski et al., 1997; Malerba & Orsenigo, 1999; Raymond et al., 2010;
Roper & Hewitt-Dundas, 2008). Recent works by Hecker and Ganter have
extended research on innovation persistence to non-technological forms of
innovation and showed significant differences in persistence patterns and
underlying innovation processes between product, process and organizational
innovation (Ganter & Hecker, 2013b; Hecker & Ganter, 2014).
With the accumulation of time series data and further methodological
advancements in longitudinal analysis, we expect the future of innovation research
to bring an increased understanding of dynamic phenomena of innovation such as
intertemporal complementarities between various innovation types, spillover and
diffusion effects or gestation lags of changes in innovation determinants. Such
increased understanding enables innovation practitioners to manage not only
singular innovation projects or events, but to farsightedly shape path-dependent
trajectories of innovation activities. It also helps policy makers to design
institutional environments and support programs with sustainable long-term
impact on innovation performance.
b. Managing Global Systems of Innovation
Innovators are not atomistic actors, but the external environment matters, ranging
from the immediate environment around the local office, the local and regional
environment (other firms, research institutions, policies etc.), the national
environment (institutions, labor market, policies), to the global environment.
Whilst regional and national innovation systems have received much attention, it
has been increasingly acknowledged that global networks and global innovation
systems have become vital. Previous research suggests that global networks are
critical for innovation, whilst too much emphasis on the local/regional level can be
problematic (Fitjar & Huber, 2015; Huber, 2012a).
It will be a key challenge for innovation management to evaluate the strengths
and weaknesses of the external environment at various spatial scales and to engage
with the local, regional, national and global environment in a targeted and effective
manner. Importantly, the positioning in innovation systems at multiple spatial

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scales can contribute to a structured approach to open innovation. Whilst the
literature on open innovation and the innovation systems literature have largely
been developing separately, there is potential to combine both perspectives.
Granted, understanding the nature and role of different kinds of innovation
systems, from technological innovation systems, regional innovation systems,
national innovation systems and global production networks and other
transnational linkages is challenging (Bergek et al., 2015; Binz, Truffer & Coenen,
2014). Evaluating these complexities for a specific innovation, as well as
developing a strategy for positioning, in most cases will be too demanding for an
individual and therefore also requires learning in broader networks.
Our empirical research suggests that the local/regional environment tends to be
most important for sourcing business related knowledge for innovation, while the
global networks tend to be critical for acquiring cutting-edge technological
knowledge (Huber, 2013, 2012a). A key question is how to establish and maintain
global networks. Our research suggests that alternative types of proximity such as
social proximity, organizational proximity or institutional proximity can substitute
for a lack of geographical proximity (Fitjar, Huber & Rodriguez-Pose, 2015;
Huber, 2012b). For individual firms as well as for innovation policy, facilitating
the establishment and maintenance of targeted international networks will be a
critical factor for successful innovation. For instance, booking flights to establish
temporary proximity (Bathelt & Schuldt, 2008) may be more fruitful than spending
too much precious time with unstructured regional networking. Furthermore, in
particular for technological dimensions in several sectors, participation in global
networks can also be in virtual forms such as online discussion forums or social
media, where innovators can benefit from targeted knowledge sourcing as well as
from unstructured ‘virtual buzz’ (Bathelt & Turi, 2013). Importantly, a promising
perspective for entrepreneurial teams and firms is to develop a consistent strategy
of division of labor considering the variety of knowledge sourcing and
collaboration opportunities for different functions, notably for exploration versus
exploitation.
c. Managing Innovation for Environmental Sustainability
Environmental challenges such as climate change, environmental pollution or
water shortage ironically involve an attractive side effect for business: they will
generate considerable demand for new products, services and other kinds of
innovation which helps addressing these issues. In addition to intelligent public
policies and an entrepreneurial state (Mazzucato, 2013), there is an important role
for the private sector to develop innovative solutions which help addressing the
environmental problems. Environmental dimensions will increasingly become

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important for firm competitiveness because of (i) environmental challenges and
related tightening of environmental regulations, (ii) many scarce natural resources
getting more expensive, and (iii) the increasing transparency of environmental
activities where there is nowhere to hide and news travel fast in an open digital
world (Winston, 2014). Future innovators will need to understand the range of
strategic options of the eco-advantage playbook (Esty & Winston, 2009). The
review by Adams, Jeanrenaud, Bessant, Denyer & Overy (2015) provides a useful
overview of these sustainability1 oriented options for innovation (see Table 18.1).
Table 18.1. Model of sustainability oriented innovation (Adams et al., 2015).
Approach
Operational
Optimization
“Eco-Efficiency”
Organizational
Transformation
“New Market
Opportunities”
Systems Building
“Societal Change”
Innovation Objective
Compliance,
efficiency
“Doing the same
things better”
Novel products,
services or business
models
“Doing good by
doing new things”
Novel products,
services or business
models that are
impossible to achieve
alone
“Doing good by
doing new things
with others”
Innovation Outcome Reduces harm Creates shared value Creates net positive
impact
Innovation’s
Relationship to the
Firm
Incremental
improvements to
business as usual
Fundamental shift in
firm purpose
Extends beyond the
firm to drive
institutional change
A first approach is about improving eco-efficiency through operational
optimization. This involves incremental innovation to reduce environmental harm
per unit of production with a view of either pro-actively reducing economic costs
or reactively complying with new environmentally driven regulatory
environments. The business model remains unchanged but firm competitiveness
can be gradually improved. Internal mechanisms for operational optimization have
already been adopted by many firms, but there is considerable scope to fully
embrace the environmental dimension, for instance by improving awareness and
involvement of employees, for creative solutions in the future.
1 Whilst the term sustainability involves multiple dimensions and is often used as a fuzzy term, for
the purposes of this paper we will focus on the environmental aspect of sustainability. We understand
green innovations as those that either reduce environmental pollution or enable the use of renewable
sources of energy.

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A second approach is more demanding and involves a fundamental
organizational transformation to pursue new market opportunities. Here, the focus
is on developing products, services or business models which improve
environmental performance and serve customer needs. Ideally, if one can help
customers to address a need, want or desire whilst helping the environment, there
is considerable scope for financially successful business models (Esty &Winston,
2009). This usually requires considerable engagement with external stakeholders
and a strong support from senior management across departments (Adams et al.,
2015). Considering environmental sustainability in business strategy has become
mainstream (Kiron, Kruschwitz, Haanaes, & Von Streng Velken, 2012) and most
large companies integrate it into their innovation activities. Yet, the scope of
environmental innovation varies and future innovators will need to convince
increasingly critical or indifferent consumers and the public that their activities go
beyond ‘greenwashing’ (Delmas & Burbano, 2011).
The third approach is the most demanding because it shifts the focus to a more
systemic level, where environmental innovation cannot be done by one
organization alone but has to be developed in cooperation with a range of actors.
As the literature on the multi-level of sustainability transitions has illustrated
(Geels, 2004; Smith, Voss, & Grin, 2011), the greening of socio-technical regimes
requires institutional change by a range of related actors (engineers, business
people, end users, and policy makers etc.). Socio-technical regimes stabilise
existing trajectories in terms of “cognitive routines”, “regulations and standards”,
“adaptation of lifestyles to technical systems” and “sunk investments in machines,
infrastructures, and competencies” (Geels & Schot, 2007). As a consequence, the
successful change or replacement of dominant regimes requires a change on
several fronts. This also implies that a focus on technological innovation as such
is never sufficient but technology has to be understood within broader social
contexts. This type of systemic eco-innovation requires inter-organizational
collaboration and active engagement with stakeholders.
One of the challenges is how to monitor and consider changing environmental
legislation and policies and pro-actively shape them. Whilst Fagerbert, Laestadius
& Martin (2015) rightly argue that new environmental policies which stimulate
eco-innovation will be vital for the future of Europe and the world, there is
considerable uncertainty as to how this will pan out in time and space. Future
innovation for sustainability needs to be sensitive towards regulatory changes in
different cities, regions and countries. Pro-active engagement with policy makers
in the forms of lobbying or participation with political pilot schemes can be
decisive activities for the growth stage.
Furthermore, establishing mechanisms to inform and convince consumers to
use green alternatives is a critical hurdle. Proponents of sustainability transitions

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often argue that the consumers need to accept constraints and restrictions, and
simply “(c)atering to people’s desire for comfort, convenience and low cost may
not lead sustainability transitions. In our view, sustainability transitions require
that people accept constraints and are willing to live and behave differently”
(Kemp & Van Lente, 2011, p. 124). However, within a pleasure-seeking
consumer-centered society, prohibiting ‘lavish’ lifestyles which the consumers
enjoy is often difficult or even unrealistic. Against this backdrop, it will be a key
challenge to develop value propositions which are compatible with the consumers’
needs, wants and desires. For this purpose, collaboration with organizations which
control complementary assets for convincing consumers to use the green
alternatives may often be vital.
Creative linkages with entertainment industries may offer one avenue. In our
ongoing research, we have investigated the potential contribution of motorsport
for the emergence and diffusion of clean technologies. Motorsport’s focus on
maximizing speed and efficiency could potentially be directed towards green
innovation outside of motorsport. Green innovation in motorsport can act as an
important vehicle to increase the attractiveness of green cars. Yet our research
shows that bridging motorsport and non-motorsport requires the right institutional
settings: First, motorsport regulation needs to provide explicit incentives for
developing solutions that reduce the use of natural resources or enable the use of
renewable energy sources. Second, public funding arrangements are needed to
provide further incentives for motorsport and cleantech to come together and
collaborate. This confirms the argument by Mazzucato (2013) that (i) networks
matter for innovation and government has responsibility for facilitating networks,
and that (ii) government funding plays an important role for enabling risk-taking
and growth fostering innovation.
Granted, the debate on corporate sustainability often tends to over-emphasize
win-win situations without carefully considering potential trade-offs (Hahn, Figge,
Pinkse & Preuss, 2010). Corporate success, environmental sustainability and social
responsibility do not always go hand in hand and the future innovation
management should show sensitivity towards this whilst pursuing realistic
approaches. It should be mentioned that it is a contested question to what extent
eco-innovation can lead to true ecological sustainability within the context of a
capitalist, growth-based economy (Bowen & Hepburn, 2014; Jackson, 2009;
Jordan, 2008). As nearly all forms of consumption currently contribute to
greenhouse gas production or environmental pollution, this question will depend
on the challenge of producing energy and electricity out of renewable sources.

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d. Managing Digital Innovation
An additional trend, which will inevitably shape the future of innovation
management, is the digitalization of the economy. In line with many
commentators, we believe that this will transform nearly all existing industries and
it will shape the opportunity space for new start-ups. Therefore, competence in
integrating the digital dimension into innovation management will be vital. On the
basis of their research Westerman, Bonnet, & McAfee (2014) show that even non-
technology-driven industries (for instance, finance, manufacturing or
pharmaceuticals) require digital mastery to develop more profitable businesses.
Digital mastery includes the dimensions of building digital capabilities and
building leadership capabilities. Building digital capabilities has to focus on
integrating digital technologies into (i) creating a compelling customer experience,
(ii) developing core operations and (iii) reinventing business models. Successful
digital transformation also requires developing appropriate leadership and
organizational capabilities for crafting a digital vision, engaging the whole
organization, governing the transformation and building technology leadership
capabilities (Westerman et al., 2014).
The issue of developing economically viable business models is also a key
challenge for new start-ups who are centered on a purely digital product or service
(Sawy & Pereira, 2013; Weill & Woerner, 2013). It has become difficult to build
a business model merely on intellectual property, and alternative sources for
revenue generation will become critical. In an online world much is expected to be
free, and this obviously is a key hurdle for successful innovators. For instance,
making money out of publicly available open data epitomizes this challenge. Here,
creative open innovation strategies can be the basis for viable business models. For
example, issuing open data can create awareness and ‘traffic’, which can be
utilized for selling alternative products/services, for advertising revenues or for
access to complementary data. Creating value out of ‘big data’ can provide further
opportunities for business models (Davenport, 2014; Walker, 2015). Here, the
technical capability of warehousing data, linking data and applying smart
mathematical methods of data analytics for analysis, prediction and prescription
has to be translated into viable value propositions and robust monetization.
Furthermore, the digital space also enables new forms of coordination between
supply and demand as the emerging sharing economy illustrates (Belk, 2014;
Botsman & Rogers, 2010; Grinevich, Huber, Baines & Eder, 2015). This facilities
economic transactions between strangers and has the potential to transform a range
of industries. As Frenken, Meelen, Arets & Glind (2015) have clarified, the sharing
economy in a narrow sense is about “consumers granting each other temporary
access to under-utilized physical assets (“idle capacity”), possibly for money”.

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This is in competition with other types of ‘sharing’ in the form of the second-hand
economy, the on-demand economy and the product-service economy. Importantly,
for the future of innovation management it is important to disentangle, and to
understand the different types and functioning of sharing mechanisms. In this
space, future innovation has to develop more sophisticated mechanisms of
establishing trust among strangers via more elaborate online rating mechanisms
and safety procedures.
Overall, the capability to evaluate and engage with emerging political
regulations, develop and communicate attractive value propositions and establish
effective open innovation strategies will be essential for successful innovation
management in the digital future. Importantly, digital innovation will not only
concern purely digital products but will infiltrate all industries. With new
technological trends such as the Internet of Things (IoT) and Industry 4.0 etc. we
will see an integration of digital and physical objects in production, distribution
and consumption processes, which are transforming competition (Porter &
Heppelman, 2014). Whilst many small- and medium sized companies in traditional
industries, including the German and Austrian ‘Mittelstand’, have become curious
observers of these ongoing trends, they will need to proactively embrace them in
order to remain competitive in the future.
4. Conclusion
This chapter has sought to reflect on selected key topics and challenges for
innovation management in the foreseeable future on the basis of our own research
fields. We highlighted that research and practice still requires more engagement
with non-technological forms of innovation as well as the dynamic context of
innovation. Also, we argued that navigating through global systems of innovation
will be a critical challenge. Furthermore, we elaborated on two trends that will
generate considerable demands for innovation in the future: ecologically
sustainable solutions and digital innovation. Each of these trends requires specific
knowledge about the strategic landscape as well as specific operational knowledge
and competencies. Due to the scope of the chapter, we could only touch upon
selected issues. Again without any claim to completeness, other important topics
for the future of innovation management include organizational forms of co-
creation between large companies and start-ups (Docherty, 2015), constraints
driving innovation such as frugal innovation (Rao, 2013), reverse innovation as
innovation from a developing country later introduced in an advanced country
(Zedtwitz, Corsi, Søberg, & Frega, 2015) or inclusive innovation which benefits
the disenfranchised (George, McGahan & Prabhu, 2012).

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The future of innovation management will face considerable challenges
because of the high pace and often high complexities of technological, social and
economic change. A first consequence of this is that hardly any individual
organization will have all the knowledge and capabilities for developing successful
innovations. In the vast majority of cases, future innovation will depend on
effective interactions between different organizations and bringing together of
complementary knowledge and capabilities. In such innovation systems, it is not
only private companies with their talent, creative culture and organizational
competencies that will be driving innovation; also public institutions and the state
will proactively shape the innovation trajectories (Bowen & Hepburn, 2014;
Mazzucato, 2013). Higher education establishments can add systematic research
insights and develop new qualifications for innovation but will also depend on
collaboration with the private sector to keep up with the fast changing
developments.
A second consequence of the high pace and complexities of change is that we
have to embrace failure as a ‘natural’ and necessary part of our innovation system
as only selected entrepreneurial and innovative attempts will turn out to be
economically successful. Our social, economic and political institutions need to
learn to accept, deal with and benefit from failure. Yet, this does not give
innovation managers and researchers a carte blanche to pursue random activities,
but academic research and the practice of innovation management can develop
clear strategic navigation tools and practical guidelines for dealing with an
uncertain and dynamic world.

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