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34 Int. J. Knowledge-Based Development, Vol. 4, No. 1, 2013
Copyright © 2013 Inderscience Enterprises Ltd.
The knowledge-innovation nexus in the welfare
service ecosystem
Harri Jalonen
Turku University of Applied Sciences,
Lemminkäisenkatu 30, 20520 Turku, Finland
E-mail: harri.jalonen@turkuamk.fi
Abstract: This paper makes two contributions on knowledge-intensive
innovation research. Firstly, it identifies and elaborates on the knowledge
problems present in the welfare service’s innovation ecosystem. Secondly,
drawing on relevant theoretical approaches, this paper discusses the managerial
implications these knowledge problems pose for stakeholders involved in
innovating welfare services. The identified knowledge problems are
uncertainty, complexity and ambiguity; uncertainty refers to a lack of
information or factual knowledge about something, complexity arises from the
intricacy and connectivity of the various elements, and ambiguity stems from
multiple interpretations. The paper also presents the managerial implications
for each type of knowledge problem; uncertainty can be reduced by the
acquisition of more information and knowledge increased by creating
connections within the ecosystem, complexity can be minimised by increasing
knowledge capacity and decomposing problems, and ambiguity can be
addressed by structuring the unknown into a frame of reference.
Keywords: knowledge; innovation; uncertainty; complexity; ambiguity;
ecosystem; welfare services; knowledge problem.
Reference to this paper should be made as follows: Jalonen, H. (2013)
‘The knowledge-innovation nexus in the welfare service ecosystem’, Int. J.
Knowledge-Based Development, Vol. 4, No. 1, pp.34–49.
Biographical notes: Harri Jalonen holds a PhD in Knowledge Management
from Tampere University of Technology, Finland. He has worked at Turku
University of Applied Sciences since 2001. Currently, he works as a Principal
Lecturer at Turku University of Applied Sciences. He has long-term research
experience dealing with knowledge and innovation management issues in
different organisational contexts.
1 Introduction
Seemingly, innovation serves nowadays as a kind of conceptual attractor which takes
notice of the challenges posed by a knowledge-based society and by global economic
competitiveness between firms, regions, countries and even continents. As a practice,
innovation is typically seen in an affirmative light. It is, with little question, identified
with progress and the improvement of a particular state of affairs. Innovation is viewed as
a good thing because the new idea must be useful – profitable, constructive, or solve a
problem (Van de Ven, 1986).
The knowledge-innovation nexus in the welfare service ecosystem 35
Closely linked, even if not synonymous with innovation, is knowledge. Regardless of
the type of innovation (e.g., product, service, process, organisational, systemic), the
novelty of innovation (e.g., incremental vs. radical), or the context of innovation (e.g.,
private vs. public sector) the role of knowledge is seen as crucial (e.g., Dewar and
Dutton, 1986; Nonaka and Takeuchi, 1995; McAdam, 2000; Dobni, 2006; Hyonsoo,
2009; den Hertog et al., 2010). An often repeated phrase that innovation economy is a
knowledge economy refers to the perception that knowledge is the principal source of
innovation (e.g., Johannessen et al., 1999). In addition to knowledge intensity, modern
innovation literature claims that innovation takes place in cooperative settings between
different stakeholders including private companies, public organisations, research
institutions and customers. An innovation ecosystem concept has been launched for
making dependencies and flows between different stakeholders visible (Adner, 2006). At
best, an innovation ecosystem enables, for example, participants to work across
organisational boundaries, focus on customer value creation, respond quickly to shifts in
demand and be adaptable to change. Innovation literature also emphasises the importance
of service innovations instead of product innovations. Economies are in transition from
industrial production to the knowledge-based service economy of the future. It is widely
accepted that service as an end result differs fundamentally from product (e.g., Hyonsoo,
2009). Finally, innovation is seen as a universal challenge which concerns not only
private companies but also public sectors organisations (Vigoda-Gadot et al., 2008).
Governments around the world have been exhorted to be more innovative not only in
service delivery, but also in all facets of policy formulation and development. In the
OECD countries, the greatest challenges faced by their public welfare services are their
ageing populations, the increasing cost of welfare services and changing expectations.
Despite broad agreement on the need for innovation in welfare services, however,
many governments and authorities in developed countries still face a lack of
understanding of the fundamental challenges of managing innovations. The reasons for
this are probably as diverse as the organisations themselves and people using them, but
this paper presupposes that one possible explanation could be that innovation has been
viewed rather uncritically as a process in which various problems are solved by managing
knowledge. Although the relationship between knowledge and innovation seems well
established (see next section), one can argue that many of the current approaches to
knowledge management oversimplify or at least dismiss some important characteristics
of innovation. They do so because they do not recognise the underlying knowledge
problems the innovation is intended to solve.
The goal of this paper is to identify and discuss some innovation-related knowledge
problems and their solutions in a context of welfare services. In doing so, this paper
makes two contributions to knowledge-intensive innovation research. Firstly, it identifies
and elaborates on the knowledge problems present in the welfare service’s innovation
ecosystem. Although there have been studies which have discussed the pros and cons of
an innovation ecosystem in general (e.g., Adner, 2006), little attention has been paid so
far to the special characteristics of public welfare services, particularly the knowledge
problems within these ecosystems. Secondly, drawing on relevant theoretical approaches,
this paper proposes and discusses the managerial implications these knowledge problems
pose for the stakeholders involved in innovating welfare services.
Although the arguments are developed mainly from the literature, this paper also
utilises the author’s experience from two case studies related to innovation challenges in
36 H. Jalonen
welfare services. The aim of the first case study was to understand the factors which
enable and/or hinder the presentation of new ideas and their implementation in the
welfare service system, whereas the aim of the second case study was to analyse
management practices that can be used in supporting the adoption of technology-based
innovation in elderly care. Although the case studies had different objectives, they both
touched on the relationship between knowledge (management) and innovation in
complex welfare service settings. However, experience gained from those case studies is
not used to prove the framework – it is meant to illustrate the problems and the
managerial responses embedded within the framework.
This paper is organised as follows. Section 2 offers a brief introduction to the concept
of innovation ecosystem and elaborates the interconnectivity of knowledge and
innovation. In Section 3, three knowledge problems – uncertainty, complexity and
ambiguity – are identified and discussed. Section 4 gives theoretically sound
interpretations of the possible answers to the three knowledge problems. Finally,
conclusions are drawn in Section 5.
2 An ecosystem view for knowledge-intensive welfare service innovation
It has become acceptable to claim that a post-industrial economy is knowledge-intensive
by nature. One of the most famous researchers who laid the basis for the claims of the
importance of knowledge in modern societies was Machlup (1962), who studied, among
other things, knowledge industries in terms of GDP in order to demonstrate their
importance in the modern economy (Leppälä, 2011). Later on, Florida and Kenney
(1993) have characterised the development of an economy as “undergoing an epochal
transformation from a mass production system where the principal source of value was
human labour to a new era of ‘innovation-mediated production’ where the principal
component of value creation, productivity and economic growth is knowledge”
(italicisation added). Since the days of Machlup, economists’ and other social scientists’
interest in knowledge has grown significantly (Foray, 2004). From a knowledge-economy
perspective, knowledge is seen not only as the raw material or the means of production
but also as the actual end-product (e.g., Demarest, 1997). It is a property of physical and
social capital, routines and processes, and organisational cultures as well as of individual
people (Käpylä et al., 2010).
Having said that, knowledge-intensity is increasing therefore, it is logical that
knowledge and especially its management has also become a popular research
theme in recent decades. Regardless of small differences in nuance (mostly lingual),
knowledge management is defined as a process which consists of several activities,
such as knowledge creation/construction, knowledge storage/embodiment, knowledge
transfer/dissemination and knowledge use (e.g., Demarest, 1997; McAdam, 2000; Alavi,
2001). Although there are copious potential targets for the application of knowledge
management activities, the common denominator for them is that they are commercially
motivated. It means that instead of philosophical or epistemological knowledge, the most
valuable form of knowledge from a managerial perspective is typically judged as
knowledge that works (Demarest, 1997). Adapting Edelenbos et al. (2011; originally
Jasanoff, 1990), it can be argued that what matters is that knowledge becomes a
‘serviceable truth’ – “a state of knowledge that satisfies tests of scientific acceptability
The knowledge-innovation nexus in the welfare service ecosystem 37
and supports reasoned decision-making, but also assures those exposed to risk that their
interests have not been sacrificed on the altar of an impossible scientific certainty”.
Bearing in mind that the concept of knowledge is a question for which an entire
domain of philosophy and epistemology have largely been devoted to from the days of
Platon, who himself equalled knowledge to ‘justified true belief’, from the innovation
perspective knowledge conveys a narrower meaning. Richardson (1960), for example,
has divided knowledge into technical and market knowledge in a way that can be used in
elaborating the role of knowledge in innovation. Based on the division made by
Richardson (1960), Leppälä (2011) refers with technical knowledge to the generation,
dissemination and use of knowledge in order to increase “production possibilities
regarding the development of new products or services”, whereas market knowledge
includes “knowledge about the available resources and the market in general, such as
consumers’ preferences, rival products and the actions and beliefs of competitors”.
Richardson’s and Leppälä’s emphasis on ‘what-works’ orientation to knowledge
coincides with the basic idea of innovation, which goes beyond being just a new idea.
Actually, it echoes Schumpeter’s (1942) classical definition of innovation as a
commercial application of an idea. To be regarded as innovation, a new idea must be
implemented.
Based on the above-mentioned, this paper defines innovation as a process of
formulating problems and creating new knowledge to solve them (cf. Nonaka, 1994). The
value of knowledge is thus instrumental-knowledge is needed to achieve competitiveness
through innovation rather than an intrinsic goal per se (e.g., Asheim and Coenen, 2005).
According to Dobni (2006), knowledge fosters innovation, “as it affords an organisation
both offensive and defensive positioning options”.
It has also become commonplace to assert that knowledge-economy is becoming
service-dominated. Innovation literature also emphasises the importance of service
innovations instead of product innovations. The need for service innovation is seen as
crucial particularly since service business accounts for 70% of aggregate production and
employment in the OECD nations. Within service business the most important role is
played by so called knowledge-intensive business services (KIBS). Den Hertog (2000),
and Muller and Zenker (2001), among others, have pointed out that KIBS are important
for innovation processes since they function as facilitators and sources of innovation.
Moreover, the importance of their role is increasing when the focus of innovation lies
pronouncedly in cooperation between different organisations. It is widely accepted that
service as an end result differs fundamentally from product. According to Hill (1977),
and Vargo and Lusch (2004), there are four specific characteristics which make services
uniquely different from goods. These special characteristics are intangibility,
inseparability, heterogeneity, and perishability. Presumably, these characteristics also
have implications for managing innovation – particularly managing knowledge in service
innovation (Storey and Kelly, 2002).
Furthermore, innovation is seen as a universal challenge which concerns not only
private companies but also public sector organisations (Vigoda-Gadot et al., 2008).
Parsons (2006), for example, has conceived ‘innovation speech’ as one outcome of the
rapid rise of new public management (NPM) since the late 1980s. In OECD countries, the
greatest challenges facing public welfare services are their ageing populations and the
increasing costs of welfare services. At the same time, there is a growing demand for
38 H. Jalonen
more individual and customer-oriented services. It is argued, that citizens now expect to
be directly involved in designing public services.
Modern innovation literature claims that innovation takes place in cooperative
settings between different stakeholders including private companies, public organisations,
research institutions and customers. Whether these interactions are called innovation
networks (e.g., von Hippel, 2007), innovation clusters (e.g., Porter, 2000), regional
innovation systems (e.g., Asheim and Coenen, 2005) or innovation ecosystems (e.g.,
Carayannis and Campbell, 2009) the rationale for this cooperation is that as the world has
become more complex, organisations are obliged to develop new courses of action. All
the above-mentioned modes of cooperation are built on the thinking that innovations are
created best when capabilities and ideas possessed by different stakeholders are
combined.
This paper advocates the ecosystem view for service innovation for four reasons.
Firstly, it offers an alternative to so-called ‘waterfall’ and ‘stage-gate’ models. In the
waterfall model, innovation-oriented industrial research feeds on a steady flow of basic
scientific knowledge and data generated by upstream institutions like universities, public
labs, and foundations (e.g., Jaffe et al., 2007), whereas the stage-gate model emphasises
the manageability of an innovation process that consists of several stages such as
discovery, scoping, business case development, testing and validation, launch and
post-launch review (e.g., Cooper, 1993; Lempiälä, 2011). In contrast to the rational and
linear process approach, the ecosystem view emphasises the complex and non-linear
nature of the innovation process (cf. Mitleton-Kelly, 2006). Secondly, the ecosystem
approach challenges traditional cluster thinking which sees value chains mainly as
regionally based as their strength comes from geographical proximity (Hautamäki, 2010).
In a service-oriented knowledge economy, value chains are increasingly global. Thirdly,
the ecosystem approach incorporates customers as co-innovators. Customer-orientation in
innovation is outstandingly important in services (e.g., Grönroos, 2009), because they
participate and influence both the production and the outputs, for example, by providing
information about their needs (e.g., Lönnqvist and Laihonen, 2011). Fourthly, the
ecosystem approach acknowledges that despite a shared objective, e.g., the development
of low-threshold health services, different stakeholders are continuously not only
cooperating with each other but also competing for scarce resources (Laihonen, 2012).
The success of the ecosystem depends on how it strikes a balance between multiple and
sometimes conflicting goals.
For understandable reasons, existing literature cannot offer a single and unanimous
definition of an innovation ecosystem. However, by compiling information from different
sources (Mitleton-Kelly, 2006; Adner, 2006; Hautamäki, 2010), the following six
elements seem to be the essential characteristics of an innovation ecosystem. Firstly,
innovation ecosystem refers to a functional whole which is made up of all related actors
from private, public and third sectors, as well as economic, cultural and legal institutions.
Secondly, the elements are strongly connected with each other. Connectivity and
interdependence points out that actions by any individual may affect (constrain or enable)
related individuals and systems. Connectivity of elements produces emergent behaviour.
Emergence results from a process whereby each actor continually decides which other
actors it will engage with, and what information and other resources it will exchange with
them. Thirdly, an innovation ecosystem is able to self-organise, which means a more or
less spontaneous process without externally applied coercion or control. Self-organisation
consists of phases such as production uncertainty, chaos, reduction of uncertainty and,
The knowledge-innovation nexus in the welfare service ecosystem 39
finally, new organisation. Fourthly, the emergence of novelty and the system’s capacity
to self-organise are highly dependent on its diversity. Diversity is the state or quality of
being different. The diversity of the ecosystem’s parts spreads into the rest of the system
as a result of interdependencies. Fifthly, in addition to connectivity and interdependence
within the ecosystem, ecosystems are also connected to their environment. Therefore, it
can be said that an ecosystem and its environment co-evolves, with each adapting to the
other. Sixthly, an ecosystem and its environment co-evolve in non-linear way.
Non-linearity implies that the behaviour of the ecosystem may not depend on the values
of the initial conditions, i.e., minor changes can produce disproportionately major
consequences and vice versa. Non-linear behaviour is unexpected, unplanned, unfamiliar
sequences that may or may not be visible or comprehensible.
The need for the coordination and cooperation of different knowledge bases is evident
especially in the context of public welfare services where innovations are usually “based
not on a product which can be seen, but on changes in relationships – e.g., between
service providers and users, or between different parts of the organisation or its partners”
(Hartley, 2006). Instead of individual and isolated organisations, welfare service
innovations are typically invented, implemented and diffused in complex relationships
between different organisations. To understand the complexity of these relationships, this
paper suggests an ecosystem view for welfare service innovation. An ecosystem approach
for innovation is seen fruitful because it induces to pay attention to special characteristics
of knowledge. As the ecosystem is composed of several actors with their own agendas
and knowledge bases, it may provide auspicious context for knowledge spillovers – i.e.,
positive externalities based on non-rivalry, non-excludability and cumulatively of
knowledge (cf. Leppälä, 2011). Knowledge spillovers have been used to explain why
many innovation activities are locally bounded (e.g., Keller, 2002). Implicitly, the
ecosystem approach also resonates with the idea of co-produced knowledge. In doing so,
it emphasises that in addition to scientific validity, knowledge must be socially
acceptable (cf. Edelenbos et al., 2011).
However, emphasis on interaction within the welfare ecosystem begs the question
whether and how innovation can be supported by managing knowledge. Before
elaborating on that question, this paper explores what the underlying knowledge
problems within innovation are.
3 Knowledge problems in a welfare service innovation ecosystem
Based on a literature review (Jalonen, 2012) and the works of Zack (2001) and Brun et al.
(2009), this paper presents and discusses three distinct problems of processing
knowledge, which can be deemed relevant and useful in innovation – particularly when
applied to an ecosystem view. The knowledge processing problems to be discussed are
uncertainty, complexity and ambiguity. While in principal, these knowledge problems can
be related to a wide range of different subjects (e.g., technology, market, cultural,
economic and intellectual resources, management practices and institutional
arrangements); this paper focuses on the knowledge problems posed by relationships
within the welfare ecosystem. Although uncertainty, complexity and ambiguity have
induced attention among innovation scholars for decades, it seems however that there is a
shortage of research that examines the three knowledge problems and their solutions from
40 H. Jalonen
the innovation ecosystem perspective. In order to exploit the potential of knowledge
management, the ecosystem’s actors need to understand their knowledge problems
properly. At worst, a too narrow view on knowledge problems yields too narrow
knowledge solutions, which may manifest, for example, in the form of actors failing to
“actively tapping into external knowledge, seeking exchanges with customers, suppliers,
partners, citizens and competitors” (cf. Rittel, 2011).
3.1 Uncertainty as a knowledge problem
One of the most notable definitions of uncertainty is offered by Daft and Lengel (1986).
According to them, uncertainty reflects the absence of information. This resonates, for
example, with the definition made by Galbraith (1977), who has linked uncertainty with
the success of performing certain tasks. Galbraith (1977) defines uncertainty as the
difference between the amount of information required to perform a task and the amount
of information an organisation already possesses. A slightly different definition of
uncertainty comes from Ellsberg (1961). Ellsberg has introduced the concept of ‘known
uncertainty’. Known uncertainty refers to situations where key variable and outcome
probabilities are known but their factual values remain unclear. Despite small differences
in nuance, all three definitions of uncertainty draw on the notion that individuals and
organisations work in an environment where uncertainty arises from a lack of information
or factual knowledge of something.
As mentioned, the ecosystem view emphasises the customers’ role in service
innovation. Nordgren (2009), for example, has pointed out that healthcare organisations
create value in close interaction with their customers. Without questioning the
potentiality of co-creation, however, one can ask how well-public organisations and other
welfare service providers truly understand their customers’ changing needs. It seems that
many innovations in practice are primarily motivated by the need to improve the
productivity of public welfare services – instead of well-articulated customer needs. In
other words, innovations are developed under ‘known uncertainty’ where developers face
a lack of information on customer needs. In addition, there may also be lack of
information concerning the relationship between service innovation and productivity.
This is because productivity also depends on the actions taken by customers (Lönnqvist
and Laihonen, 2011).
In addition to customer-orientation, the ecosystem view stresses the relationships
between welfare service providers. The attractiveness of the ecosystem perspective is the
result of a logic that argues that the innovation challenges in welfare services are solved
by combining the complementary and substitutive capabilities possessed by different
organisations. Interaction within the welfare service ecosystem is required for both
developing new ideas and implementing them as new practices. However, given that
innovation refers to both developing new ideas and implementing them as new practices
and that the ecosystem includes not just different actors but also enacted social relations,
it is reasonable to expect that innovation has the potential to disrupt power structures and
work routines within the welfare service ecosystem. This may create an uncertainty that
stems from the lack of knowledge regarding the management practices that can be used in
supporting the development and implementation processes of new services.
The knowledge-innovation nexus in the welfare service ecosystem 41
3.2 Complexity as a knowledge problem
Complexity refers to the non-linear interactions between the parts of a larger system. The
situation can be seen as complex if the amount and intricacy of elements and
relationships that must be considered simultaneously is too large to process easily.
Following the thoughts of Simon (1969), who defined complexity as a situation where
there are “a large number of parts that interact in a nonsimple way”, Zack (2001) has
argued that, although in complex situations predictions are difficult to make, they are not
necessarily vague or unpredictable. Stacey (2010) goes a bit further, when he points out
that relationship between variables are time and context dependent, which, he argues
means that complex events are unique.
Although it is crucial to include customers to improve welfare services, it should be
borne in mind that co-production almost invariably increases the complexity of the
innovation process. The effectiveness of services, e.g., well-being, is the result of the
relationship between customers and service providers. Regardless of actions by public
organisations and other service providers, customers may behave in a way that produces
some unintended and undesirable consequences. That is to say, co-production may lead to
innovation that yields more or less than originally expected.
Having said that, innovations in the public sector are typically incremental by nature
(e.g., Newman et al., 2001). It also seems reasonable to expect that each innovation
changes only a minor part of the total service offerings available to citizens. In other
words, complexity arises from the connectivity of the service delivery system. Due to this
complexity, the true customer value of the given innovation requires that it is configured
with the service wholeness in a way that fulfils the expected needs of its customers.
Interaction within the ecosystem can be characterised as complex in the sense that it
produces emergent patterns and behaviour, which cannot be reduced to the properties of
the actors. Complexity at the ecosystem level emerges from the connectivity of various
actors and elements. Cooperation between the public, private and voluntary sectors has
created a complex service bundle that includes the perpetual novelty arising from the
interaction and connectivity of elements. It means that no one, including the most
powerful actor in the system, can singly control or plan the responsive interplay of ideas
presented by others. The result is a complexity that arises in situations in which the
connectivity of elements and perpetual novelty that must be considered simultaneously
makes them too large to process easily.
3.3 Ambiguity as a knowledge problem
Ambiguity refers to an inability to interpret or to make sense of something (Zack, 2001).
Zack has distinguished two forms of ambiguity: surface ambiguity represents having
interpretative knowledge but not being able to recall it due to insufficient informational
cues, whereas deep ambiguity refers to a lack of interpretive knowledge. Surface
ambiguity resembles (known) uncertainty, because both of them are basically based on a
lack of information. The difference between uncertainty and surface ambiguity is that the
latter concept implies that information acts as a trigger which activates interpretation,
while uncertainty emphasises information itself as a lacking resource. In deep ambiguity,
events are perceived as so new and unfamiliar that one cannot make reasonable guesses
about what is important or about what may happen (Brunsson, 1985). The third
manifestation of ambiguity arises from multiple meanings of the same thing. Daft and
42 H. Jalonen
Lengel (1986) have named this kind of ambiguity as equivocality. According to Daft and
Lengel (1986), equivocality is a state where “participants are not certain about what
questions to ask, and if questions are posed, the situation is ill-defined to the point where
a clear answer will not be forthcoming”. Ambiguity as equivocality represents a situation
where two or more meanings can be assigned to the same informational cue (Weick,
1995). In addition to multiple meanings, those interpretations may conflict with each
other.
Ambiguity may arise in a welfare service ecosystem, for example, because of
different interpretations concerning the citizens’ role as a co-innovator. Interpretations
differ on whether the citizens are capable of and/or willing to participate in the innovation
of new services. The variety of interpretations is based on different interpretive
knowledge about citizens’ abilities to meet the requirements of developing new services.
Due to a heterogeneous clientele, the developers face ‘deep ambiguity’ which cannot be
reduced by increasing available information.
While the definition of innovation as a knowledge-intensive activity implies that
decisions around innovation can be improved with better information, it should be noted
that innovation also induces issues that go beyond information and that may increase the
ambiguity of the ecosystem. This definitely holds true in innovation, which requires
mixed-sector cooperation. This is because although cooperation increases an ecosystem’s
innovation potential, it also creates complicated organisational interlacings with
conflicting values, interests and political judgements, which, in turn, may lead to a
situation where this innovation potential remains unrealised. Ambiguity stems from
multiple interpretations of the same information cue due to different values, interests and
political judgements. It means, for example, that welfare workers can deem some
innovation both as a facilitator of work processes or as a threat to existing working
methods. Additionally, administrators and decision-makers may conceptualise the
innovation as a means of improving the productivity of the welfare services, whereas
customers may fear that the innovations are introduced in order to strengthen self-service
orientation in welfare services. In a nutshell, every innovation has many faces, i.e., it
represents different things for different actors.
4 Knowledge solutions for uncertainty, complexity and ambiguity
Conceptualising the innovation knowledge problems that arise within a welfare service
ecosystem due to uncertainty, complexity and ambiguity logically begs the question of
how to respond to them.
4.1 Reducing uncertainty by increasing information and factual knowledge
Since innovation uncertainty arises from the lack of information or factual knowledge
pertaining to various matters in the welfare service ecosystem, it sounds reasonable to
suggest that uncertainty can be reduced by acquiring that additional factual information
and knowledge (Daft and Lengel, 1986). However, in an innovation context two
problems arise. Firstly, how one can know ex ante what factual information and
knowledge is actually needed. This is because the potential value contained in innovation
can be revealed only retrospectively. Secondly, in addition to the uncertainty related to
The knowledge-innovation nexus in the welfare service ecosystem 43
the substance of the information/knowledge, there is an uncertainty which pertains to the
source of the information/knowledge.
To overcome the problem of ‘know-what’ and ‘know-who’, this paper suggests
paying attention to the requisite diversity of connections within the welfare service
ecosystem. It is connectivity and interdependency that are seen essential for the
ecosystem’s evolution (cf. Luhmann, 1995), because they determine the transfer of
information and knowledge that is needed to ease the problem of uncertainty inherent in
innovation (cf. Mitleton-Kelly, 2006). The potentiality in the requisite diversity of
connections can be explained by the concepts of ‘strong’ and ‘weak’ ties (Granovetter,
1973). Strong ties manifest themselves as relationships between individuals or groups
that regard each other as similar. Weak ties, on the other hand, refer to relationships that
connect individuals and groups that usually operate in various social environments. This
paper argues that both types of ties are needed in the transmission of knowledge
throughout the innovation ecosystem. The main benefit of different ties is that they
provide access to knowledge spillovers (cf. Ahuja, 2000). Weak ties enable a varied
knowledge base, whereas strong ties promote the distribution of knowledge particularly
in situations where the knowledge is complicated and context-bound (Hansen, 1999).
Weak and strong ties also ensure information redundancy within the welfare service
ecosystem. Information redundancy is important for easing uncertainty of innovation,
because it allows for the existence of connections whose usefulness cannot be estimated
ex ante.
4.2 Minimising complexity by increasing knowledge process capacity and
decomposing problems
Although diverse connections may help to reduce the problem of uncertainty, they may
simultaneously create another problem – the problem of complexity. Complexity arises
from the intricacy and connectivity of elements. Regardless of the nuisance caused by
uncertainty related to the needs of customers or the relationship between innovation and
productivity, however, a different kind of knowledge problem derives from cooperation
per se.
Adner (2006) labels the complexity of coordinating with complementary innovators
as the ecosystem’s interdependence risk. It is a question of emergence in which the
members of the ecosystem continually decide on which other members they will engage,
and what information and other resources they share.
What is most significant is that complexity cannot be reduced simply by increasing
information or factual knowledge. This is because the confusion that people and
organisations face does not arise from an absence of information, but it is a ‘by-product
of complexity’ (cf. Appleby, 1954). By the ‘by-product of complexity’, Appleby meant
that all intended actions have potentially unintended consequences. Therefore, this paper
suggests that what is needed in addressing complexity is to focus on the knowledge
capabilities of the ecosystem’s members. This can be done by combining an increase in
knowledge processing capabilities with the process of decomposing the complexity of the
problems (cf. Zack, 2001). The first part of the equation is based on developing rules and
routines which improve the ability of the ecosystem’s members to locate, develop and
bring appropriate knowledge, expertise, and skills to bear on the issues at stake, while the
latter one rests on restructuring and redefining the problems to resemble something more
44 H. Jalonen
familiar (cf. Zack, 2001). The managerial challenge is to find a suitable balance
between the two strategies. Adapting the division of the innovation process into two
phases – initiation and implementation (Rogers, 2003), this paper proposes that
complexity in the early phase of innovation can be reduced by improving cooperation and
knowledge sharing between experts, while the complexity related to implementation can
be reduced by breaking down tasks into smaller and hence more manageable
components. In summarising the above, this paper argues that the members of the
ecosystem can respond to complexity in innovation either by increasing their capacity to
process it or by reducing the level of complexity they face (cf. Zack, 2001).
4.3 Addressing ambiguity by structuring the unknown into the frame of
reference
Having said that a welfare service ecosystem includes wide range of individuals,
organisations, institutions and material and immaterial artefacts that are interconnected
with each other, it is reasonable to claim that the complexity of innovation in the
ecosystem stems from multiple interpretations by different stakeholders. Ambiguity
manifests itself as confusion and a lack of understanding. The underlying problem in
ambiguity is the absence of ‘reference frames’ (Brun et al., 2009). It is therefore more
essential to aim at creating shared meanings and a ‘frame of reference’ (Weick, 1995), as
only information that has been identified as meaningful can lead to change (Wheatley,
1999).
It is important to point out that the shared meaning is not imposed by those ‘in
power’. To be useful for innovation, the idea of shared meaning should acknowledge that
different people have different world views and, what is more important, that they have
different abilities to see things. This kind of diversity of knowledge and values is crucial
for innovation because it allows for a ‘polyphonia of perspectives’. Hazen (1993), for
example, uses polyphony as metaphor for organisational change. According to Hazen
(1993), the metaphor of polyphony “supports inclusive change as it helps us to hear [...]
people who speak with another in their own voices”.
The idea of polyphony is particularly useful for innovation in a welfare service
ecosystem, for at least two reasons. Firstly, it admits that every actor in the ecosystem has
their own perspective on the ideas at hand. It means that the truth (if any) lies between
these multiple perspectives (cf. Stenvall et al., 2010). Therefore, it is congruent with the
wicked nature of innovation in welfare services (cf. Eppel, 2011). The concept of the
‘wicked problem’ refers to problems that have no definitive formulation; solutions are not
true or false; there is no test for a solution; every solution has a consequence; they do not
have simple causes; and they have numerous possible explanations which in turn frame
different policy responses (Rittel and Webber, 1973; Raisio, 2010). A wicked problem is
subjective in the sense that everyone can have an equally ‘right’ opinion about it. By
listening to multiple voices, however, it is possible to come across with wicked problems
and make room for different rationalities (cf. Clegg et al., 2006). Stressing the importance
of silent voices, it is possible to avoid an organisational culture that is too monological
(cf. Stenvall et al., 2010; Clegg et al., 2006), since this is widely recognised as an
obstacle to innovation (e.g., Shane, 1995). Secondly, polyphony not only refers to the fact
that everyone has their own point of view but also implies the plurality of values. The
process of tackling wicked problems in welfare services is always political, with people
mutually enabling and constraining each other (cf. Raisio, 2010). Instead of seeing
The knowledge-innovation nexus in the welfare service ecosystem 45
different perspectives as static representations that should be receptively noticed, the idea
of polyphony emphasises “the creative interaction of contradictory and different voices”
(Clegg et al., 2006).
5 Conclusions
This paper has identified three knowledge problems related to innovation in the welfare
service ecosystem. Firstly, there is uncertainty which refers to the lack of information or
factual knowledge of something. Secondly, complexity arises from the intricacy and
connectivity of the elements within the welfare ecosystem and between the ecosystem
and its environment. Thirdly, ambiguity contains the idea that information cues trigger
multiple interpretations. After problem identification, the knowledge solutions for each
type of problem have been introduced and discussed. In the case of uncertainty, an
action’s focus should be on creating strong and weak connections which enable the
acquisition of information and knowledge possessed by the multiple stakeholders within
the ecosystem. Complexity, in turn, is not solved solely by increasing available
information, but by improving the capabilities of the ecosystem’s members in processing
knowledge and decomposing the problems they face. Finally, addressing ambiguity is
based on the admission that different people have a ‘polyphonia of perspectives’ and
therefore, focus should be put on structuring the unknown into the frame of reference,
i.e., placing stimuli into something meaningful. The three knowledge problems and their
solutions are summarised below in Table 1.
Table 1 Knowledge problems of innovation and their managerial implications
Knowledge problem Manifestation of the problem Knowledge solutions
Uncertainty Lack of information or factual
knowledge
Building a diverse set of
connections within the ecosystem
Complexity Connectivity and interdependence
between elements and the
phenomena within the ecosystem
Increasing knowledge capacity
and decomposing complex
problems
Ambiguity Ecosystem members’ multiple and
conflicting interpretations
Promoting interpretation and
reframing by accepting the
‘polyphonia of perspectives’
The commonly held belief is that uncertainty, complexity and ambiguity are negative
issues, which, if possible, should be avoided. Contrary to that, this paper argues that they
are inherent characteristics of every innovation. Innovation is a hypothesis, whose truth
cannot be established with certainty (Hurst, 1982). However, this paper points out that
understanding knowledge problems properly is crucial for innovation – especially in the
context of a connected ecosystem. This is because innovations emerge from complex
intra- and inter-organisational processes in which dispersed knowledge is combined and
new knowledge is created. Misjudging the underlying knowledge problem may have a
detrimental effect on innovation practice. By way of example: If the underlying
knowledge problem during the innovation process is multiple interpretations, it is not
possible to resort to a simple question-answer type of communication in the management
of ambiguity. Ambiguity is full of multidimensional problems, the capture of which
cannot be promoted by increasing the amount of information.
46 H. Jalonen
This paper suggests that the effectiveness of managing knowledge within a welfare
innovation ecosystem depends on its members’ ability to possess attributes that are
paradoxical, i.e., simultaneously contradictory, even mutually exclusive (cf. Cameron,
1986). According to Cameron (1986), a paradox is a state where “no choice need be
made between two or more contradictions”. In a paradox, contradictions are accepted and
present and they operate simultaneously (Cameron, 1986). Applied to different
knowledge problems inherent in innovation, this could mean that the effectiveness of
their management is dependent upon the presence of the paradox of thinking both
convergently and divergently (cf. Schumacher, 1977). Convergent thinking is suitable for
problems of uncertainty and complexity, whereas in ambiguity divergent thinking is
needed. Adapting Schumacher (1977), this paper concludes that there is ‘nothing wrong’
with solving problems of uncertainty and complexity. Instead problems which arise from
ambiguity “cannot be solved in the sense of establishing a correct formula”, but,
however, they ‘can be transcended’. This is because unlike uncertainty and complexity
(convergent problems), ambiguity (divergent problems) deals with a “higher level of
being” (cf. Schumacher, 1977). Finally, it is important to note that arguments presented
in this paper are indicative in nature. Obviously, further research should be carried out to
validate the arguments.
Acknowledgements
This paper is written partly as a contribution to the research and development project:
Virtual Elderly Care Services on the Baltic Islands (funded by the European Regional
Development Fund and Turku University of Applied Sciences), partly as a contribution to
the research programme: Management of the Value Network of Social Services in the City
of Helsinki (funded by The Finnish Work Environment Fund and Turku University of
Applied Sciences).
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