Data, information and knowledge in regional innovation networks

Data, information and knowledgein regional innovation networks

Quality considerations and brokeragefunctions

Helina Melkas and Vesa HarmaakorpiLappeenranta University of Technology, Lahti, Finland

Abstract

Purpose – The purpose of this article is to investigate data, information and knowledge in regionalinnovation networks. Emphasis has been put recently on regional innovation systems, where variousactors are involved in innovative processes. The article responds to the need to study matters relatedto knowledge management and information quality in such environments.

Design/methodology/approach – Regional innovation networks and data, information andknowledge as well as research on them are discussed at a theoretical level. An existing innovationnetwork of the Lahti region, Finland, was utilised as a pilot environment when building the knowledgemanagement framework that is introduced. The framework is based on established knowledgemanagement literature and practice.

Findings – The results confirm that the aspects of data, information and knowledge need to beaddressed systematically in regional innovation networks. They are intertwined with knowledgemanagement and network management. The knowledge management framework introducedincorporates, apart from information quality considerations, future-oriented self-transcendingknowledge as well as knowledge vision and knowledge assets. Considerations of absorptivecapacity and information brokerage in the regional knowledge environment are emphasised.

Research limitations/implications – The limitations of the framework will be assessed in futurestudies. This will also improve understanding of practical implications. Research implications arerelated to data, information and knowledge quality – as well as absorptive capacity between the twosubsystems of the regional innovation system.

Originality/value – The article combines in a novel way research fields that have previously barelybeen combined – information quality, knowledge management and regional innovation networks. Itprovides new insights into a societally important theme and shows possible avenues of furtherresearch.

Keywords Quality, Knowledge management, Information networks, Information brokers, Finland

Paper type Literature review

Introduction: purpose and approachThe present techno-economic paradigm emphasises collective learning processes ingenerating innovations. Information is said to be the most important production factorand learning the most important process in today’s world. At the regional level, a lot offocus has been put on regional innovation systems or regional milieux, where differentkinds of actors are involved in innovative processes, profiting from the emergingexternalities during the cooperation.

In this study, the main focus is set on investigating data, information andknowledge quality as well as their relation to knowledge management in regionalinnovation networks. The research questions are:

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European Journal of InnovationManagement

Vol. 11 No. 1, 2008pp. 103-124

q Emerald Group Publishing Limited1460-1060

DOI 10.1108/14601060810845240

RQ1. What are the special characteristics of regional innovation networks in thecontext of data, information and knowledge?

RQ2. How do explicit, tacit and self-transcending knowledge and related data andinformation interact within regional innovation networks?

RQ3. How can a knowledge management framework be built for a regionalinnovation network?

RQ4. How can quality of data, information and knowledge be taken into accountwithin the knowledge management framework?

RQ5. How could knowledge acquisition, assimilation, transformation andexploitation processes (absorptive capacity) be aided in regional innovationnetworks by means of knowledge management?

The present paper is primarily a theoretical undertaking discussing regionalinnovation networks and data, information and knowledge quality. In building theknowledge management framework, empirical experiences from regional innovationnetworks of Lahti region, Finland, were utilised.

Background: regional innovation networks and systemsMetafora: a regional innovation network – like a football teamRegional innovation networks can be compared to football teams. A football team hasto follow certain rules of the game, and the team must create common tactics to be ableto achieve the goals that have been set. Even though the rules are given and tactics arecreated, players have to interact creatively with each other in the game. During theseason, players get to know the other players of the team better, which facilitatesimprovement of the tactics. It is a matter of collective learning.

Interaction between the players on the field occurs mainly by serving the ball to thecolleagues in the team. In order to follow well the tactics created, exact passes are needed.To be able to give a good pass, you have to know how the pass is given technically.However, you also have to be able to adjust the pass according to the receiver. The timingof the pass is highly important. One certainly should give different kind of passes to a fastand technical player than to a slower player with the ability to score goals by head.

A knowledge management system of an innovation network is similar to rules andtactics of a football game, as it enables collective learning to take place in order toincrease the capability of the network. The quality of giving and receiving a passequals very well to the quality of giving and receiving information in an innovationnetwork. Even if the knowledge management system were correctly built, bad data,information and knowledge quality would destroy the collective learning process in theinnovation network – just like bad passes would destroy the game, even if the tacticswere correctly designed. This is what we are investigating in this article.

Regional innovation systems and networksInnovations – whether they are radical technological advancements or incrementalsocial and organisational changes – are often done in networks, where actors ofdifferent backgrounds are involved in the process, setting new demands forinnovativeness. The science push effect as the driving force of innovations is an

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exception rather than a rule in these processes (Schienstock and Hamalainen, 2001). Amore influential source of innovations seems to be factors like the ability to interact,learn collectively and build trustful relations between the innovating partners(Harmaakorpi, 2004).

Characterising innovation as a socially and economically embedded process raises aquestion of the socio-institutional environment where innovation processes are takingplace. In a regional context, innovation is seen as a process embedded in a regionalinnovation system (see, e.g. Cooke et al., 1997; Storper, 1997; Braczyk et al., 1998; de laMothe et al., 1998; Doloreux, 2002). A regional innovation system is understood as asystem of innovation networks located within a certain geographic area, in which firmsand other organisations are systematically engaged in interactive and collectivelearning through an institutional milieu characterised by embeddedness (cf. Cookeet al., 1998; Kostiainen, 2002). Collective learning is a process of dynamic andcumulative knowledge creation that has many synergy advantages due to itsinteractive character (Camagni, 1995). A regional innovation system consists ofdifferent kinds of multi-actor innovation networks including actors with often verydifferent aims and knowledge interests.

Gibbons et al. (1994) define two classes of knowledge used in innovation processes.Mode 1 is hierarchical and tends to preserve its form, while Mode 2 is moreheterarchical and transient by nature. One of the key contrasts between the two modesis that in Mode 1, problem solving is carried out following codes of practice relevant toa particular discipline and problem solving, while in Mode 2, knowledge activity isorganised around a particular application and is more diffuse by nature. Gibbons et al.(1994) report an epoch change in knowledge activity in innovation networks with ashift from Mode 1 to Mode 2 knowledge creation. (Howells, 2000.) In this study, theoften very practice-oriented Mode 2 knowledge production is seen as the main“business” of regional innovation networks.

A regional innovation system consists of innovative networks with different kind ofsocial relationships. Social structure, especially in the form of social networks, affectseconomical outcomes, since the networks affect the flow and the quality of theinformation (Granovetter, 2005). In his influential work, Granovetter (1973) defines theconcepts of strong ties and weak ties in social networks. Strong ties are characterisedby common norms and high network density. These strong ties are easier forinformation (and data and knowledge) quality considerations, since they normallyinclude a relatively high amount of trust, common aims and a same kind of languagefor communication. However, weak ties are reported to be more fruitful for innovations,as more novel information flows to individuals through weak ties than through strongties (Granovetter, 2005). Burt (2004) has developed the “strength of weak ties”argument further by arguing that innovations are most likely found in structural holesbetween dense network structures (see also Burt, 1992; Walker et al., 1997; Zaheer andBell, 2005). An actor able to span the structural holes in a social structure is at a higher“risk” of having good ideas: new ideas emerge from selection and synthesis across thestructural holes between groups (Burt, 2004). A regional innovation system rich instructural holes offers a lot of opportunities for new networked innovation processes.

The weak links or structural holes enabling the biggest innovation potential aresomewhat problematic in the context of information[1] quality. In order to be able toutilise the innovation potential in these structural holes, information should often be


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transferred between very research-oriented and practice-oriented partners, as well asbetween partners with totally different horizontal knowledge interests(interdisciplinarity). A remarkable part of difficulties between potential innovatingpartners stem from an information asymmetry on different sides of a structural hole(see, e.g. Montgomery, 1991). Partners on the opposite sides of the structural hole haveinformation of different quality and obtained for their own purposes.

Autio (1998, pp. 133-134) defines two subsystems in regional innovation systems:

(1) a knowledge generation and diffusion subsystem; and

(2) a knowledge application and exploitation subsystem (see Figure 1).

The former consists of four main types of institutions that all participate in theproduction and dissemination of both explicit (codified) and tacit (technological)knowledge and (technical) skills. Key elements include public research institutions,technology mediating organisations, educational institutions and workforce mediatingorganisations. The knowledge application and exploitation subsystem, again, consistsof four C’s: companies, clients, contractors and competitors. Ideally, there should behorizontal and vertical linkages among the firms. Also dialogue and interactionsbetween subsystems and actors within subsystems are a necessary prerequisite for RISto operate sufficiently (Autio, 1998; Todtling and Trippl, 2005).

Absorptive capacity in regional innovation systemsA highly important factor in spanning structural holes and overcoming informationasymmetries is absorptive capacity of the regional innovation system’s actors, its

Figure 1.Main structure of regionalinnovation systems (RIS)

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networks and of the entire system. Originally, Cohen and Levinthal (1990) definedabsorptive capacity of an organisation to be the ability to value, assimilate and applynew knowledge. Kim (1998) argues that absorptive capacity requires learningcapability and develops problem-solving skills; learning capability is the capacity toassimilate knowledge for imitation, and problem-solving skills are necessary to createnew knowledge for innovation. These works have not, however, concerned thehierarchy of data, information and knowledge.

Zahra and George (2002) define two different types of absorptive capacity that give agood point of departure for this research (see also Uotila et al., 2006). Potential absorptivecapacity is important in acquiring and assimilating external knowledge, whereasrealised absorptive capacity refers to functions of transformation and exploitation of theknowledge gathered. Both are, naturally, important in regional innovation processes:potential absorptive capacity enables exploration of knowledge (often) over the weak tiesof an innovation system, and realised absorptive capacity secures exploitation (often) inthe strong ties of innovation networks. Absorptive capacity is crucial when ponderingquestions of information quality in regional innovation networks; higher absorptivecapacity enables easier crossing of structural holes in the innovation system – that is,longer passes over the football playground (cf. the metafora).

To understand better the characteristics of absorptive capacity in innovationprocesses, we have to take a closer look at its different parts: acquisition, assimilation,transformation and exploitation. Acquisition refers to an actor’s capability to identifyand acquire externally generated information and/or knowledge that is critical to itsoperations. Assimilation refers to the actor’s routines and processes that allow it toanalyse, process, interpret and understand information obtained from external sources.Transformation denotes an actor’s capability to develop and refine routines thatfacilitate combining existing knowledge and the newly acquired and assimilatedknowledge. Exploitation as a capability is based on routines that allow actors to refine,extend and leverage existing competencies or to create new ones by incorporatingacquired and transformed knowledge to their operations (Zahra and George, 2002).According to these definitions, absorptive capacity is like a funnel, where potentialabsorptive capacity secures newness and diversity of the necessary knowledge,whereas realised absorptive capacity stands for operationalisation of the newknowledge in the existing processes in order to make an actual innovation to takeplace. Zahra and George (2002) also suggest that there is a special need for a socialinteraction mechanism between assimilation and transformation processes. A revisedmodel of Zahra and George is depicted in Figure 2.

Figure 2.Absorptive capacity of

future-oriented knowledgein innovation processes


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Information quality and regional innovation policyThe analysis beyond reveals the crucial role of information – as well as knowledge anddata – quality in enhancing functions of regional innovation systems and, therefore, inthe focus of regional innovation policies. Questions of information quality are crucialwhen trying to explore and exploit the most fruitful – and the most challenging –innovation potential luring in the weak ties and structural holes of innovationnetworks. There are several important matters concerning information quality inmulti-actor innovation networks. In addition to flows of data, information and differenttypes of knowledge, information quality needs to be addressed in a systematic way inthe networks of a regional innovation system, as it is intertwined with knowledgemanagement and network management. The reasons are as follows:

. Knowledge controls and guides decision making and other processes throughassessment of information. Awareness – that is necessary for activities infuture-oriented innovation networks – occurs only after both information andknowledge have been assessed and judged against situations and experience(Miller et al., 2001).

. Quality of information cannot be improved independently of processes thatproduced this information and of contexts in which information consumersutilise it (Strong et al., 1994). The same applies vice versa; contexts and processesof regional innovation networks cannot be improved independently of quality ofinformation. The relationship between information management and knowledgecreation is tight. Good information quality helps greatly in knowledge creation(Huang et al., 1999).

A definite line is typically drawn between data- and information-related research andresearch on knowledge management. This has led to a situation where the importantinterrelationship between these is nowadays often overlooked. A much wider view ofdata and information management than the traditional information systems andinformation technology approach is necessary, including considerations of quality, asin this article.

From data to knowledge and furtherA brief look into the theoretical basis and distinction of data, information andknowledge remains to be taken to clarify the background. Definitions of informationhave followed two patterns, either:

(1) focused on information (and knowledge) being fundamentally different fromdata (which is called the hierarchical view); or

(2) emphasised that some knowledge is needed before data and information can becreated.

In many studies data, information and knowledge are used interchangeably (Melkas,2004). Huang et al. (1999) note that in practice, managers tend to differentiateinformation from data intuitively.

Data – numbers, for instance – are the factual content of information. Onlymeaningful information can be the basis for purposeful action. When data are put in ameaningful context and processed, they become information (Lillrank, 1997, 2003).Information transforms into a component of knowledge, when it is analysed critically

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and its underlying structure is understood in relation to other pieces of information andconceptions about how the world works (cf. Miller et al., 2001). Tuomi (1999), again,argues that information can be created only after there is knowledge. Informationtheories emphasise that information is a message’s characteristic (Aberg, 2000). Wiio(1989) analysed the concept of information from the point of view of systems theory –as an act and a process.

Between information and knowledge, there is also considerable conceptualunclarity. Some researchers emphasise that despite their difference, the relationshipbetween information and knowledge is interactive (English, 1999; Huang et al., 1999).The situation is further complicated by different types of knowledge – explicit, tacitand self-transcending (see, e.g. Nonaka and Takeuchi, 1995; Scharmer, 2001; Pierceet al., 2006).

Scharmer (2001, pp. 68-69) introduced the concept of “self-transcending” knowledge,or “tacit knowledge prior to its embodiment”. It is the ability to sense the presence ofpotential, to see what does not yet exist. Scharmer describes different types ofknowledge with examples from quality management. When measuring outcomes ofquality, managers need explicit knowledge. When improving process management, theoverall focus is on knowledge in use – tacit knowledge. When improving qualities ofthought and customer experience, self-transcending knowledge is needed. (Scharmer,2001, p. 70.) It is thus a relevant concept within regional innovation networks.

Drawing the lines between the various concepts is quite problematic eventheoretically – not to talk about practice – but such discussions are crucial to form abasis for the integration of quality considerations into knowledge creation andmanagement within regional innovation networks. For instance, within knowledgemanagement, a clear distinction is traditionally made between explicit and tacitknowledge. These are not independent of each other – rather, they are mutuallycomplementary, which is once again illustrated in the case study of this article.

Those treating information and knowledge as different but relatively equal have notbeen particularly numerous until these days. One reason for the general confusionoccurring in conceptual discussions may be caused by a “chaining process” that takesplace in organisations (Miller et al., 2001). Some explicit knowledge may be treated asdata by higher level processes. Explicit knowledge also may be sent to decision-makerswho view it as information. Certain information may likewise be treated as data byhigher level processes. Miller et al. emphasise that recognising and understanding thischaining process may contribute to perceiving the complexity of the field. Theterminologies in question have been summarised as follows by Miller et al. (2001,p. 365):

. Data: A representation of an object.

. Information: The aggregation of data into something that has meaning(semantics) through interpretation by human or automated processes.

. Knowledge: That which is derived and inferred from assimilating informationagainst perceived context, experience or business rules.

. Decision: A process for arriving at a solution to a problem, using knowledge toassess and judge information.

. (Situational) awareness: The assessment of information into decisions andactions, guided by knowledge of the contextual domain.


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Quality issues for data, information and knowledgeInformation quality is not an entirely new concept, but it has gained increasing attentionduring the last few years, also in business communities. It has been studiedoverwhelmingly by researchers interested in computing, management informationsystems, databases and their management, data security and data warehouse quality, tomention a few (Melkas, 2004). The concept of data quality has earlier been used to agreater extent than the concept of information quality. Researchers have concentrated oncompany environments and business information (cf., e.g. English, 1999; Huang et al.,1999; Chengalur-Smith et al., 1999; Wang et al., 1998; Wand and Wang, 1996; Wang, 1998;Paradice and Fuerst, 1991). Studies of information quality in heterogeneous innovationnetworks consisting of organisations from different sectors have not been undertaken.Knowledge quality is a newer concept than data quality and information quality.

Conventionally, information quality has been described as how accurateinformation is. Huang et al. (1999) claim in their comprehensive “guidebook” that nostandard definition for the concept exists. English (1999, p. 27), again, representsinformation by the formula:

Information ¼ f ðData þ Definition þ PresentationÞ

These three components make up the finished product of information. English (1999,p. 24) lists also two general definitions:

(1) information quality is consistently meeting knowledge worker andend-customer expectations through information and information services,enabling them to perform their jobs efficiently and effectively; and

(2) information quality describes the attributes of the information that result incustomer satisfaction.

Wang and Strong (1996, p. 6) define “data quality” briefly as “data that are fit for useby data consumers”.

Earlier research approaches to study data and information quality have beendivided into:

. an intuitive;

. a theoretical/system; and

. an empirical approach (Wang and Strong, 1996; Huang et al., 1999).

They may also be classified as follows (for descriptions, see Melkas, 2004):. the customer requirements based approach;. the quality dimensions based approach (that is briefly described in the case

study); and. the technical quality versus negotiated quality approach.

There does not seem to be any reason for why knowledge quality could not beapproached from similar directions (cf. Pierce et al., 2006).

Knowledge management and qualityOne of the practical areas of knowledge management is the one that relates toinnovation and knowledge creation in organisations. In practice, knowledge

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management is very much about incremental change guided by long-term conceptualvision (Tuomi, 1999). Although knowledge management offers a compelling promise,some observers have declared that it is a fad that does not produce results. A surveyconducted in 108 companies showed no correlation between systematic management ofknowledge and improved bottom-line performance (Lucier and Torsilieri, 2001). Theresearchers expected to find a reasonably strong relationship, but finally realised thatthe negative result was accurate. They claim that results have been limited because theknowledge management community misreads the promise. Knowledge must beintegrated into management – not the other way around.

Rather than trying to find “the right way around”, it would be important to see – ina very detailed manner – the relationship between knowledge and management. It isalso noteworthy that experiences of knowledge management and discussion around ithave concerned companies. The research environment focused on in this article isdifferent – it is a new field of which there are no experiences, either good or bad.Regional innovation networks are an environment that is characterised by highlyfragmented information and knowledge as well as needs. A lot of discussion hasconcerned knowledge spillovers and collective learning, but also that discussion hasfocused overwhelmingly on companies. To the authors’ knowledge, these issues arebarely being investigated in local networks dealing with regional development.

According to Nonaka and Teece (2001), the important distinction betweeninformation management and knowledge management is too frequently overlooked.Rather, it seems that the important interrelationship between these is nowadays oftenoverlooked. This may be one of the reasons for practical problems in organisations andnetworks (Uotila and Melkas, 2003). It is essential that knowledge managementprogrammes of the future are combined with management of information quality –otherwise their foundation remains incomplete (cf. Huang et al., 1999). Althoughexplicit knowledge – information – represents only part of an iceberg, this part isimportant within innovation networks such as those focused on in the present article.There is likely to be a lot of tacit and self-transcending knowledge in innovationnetworks, but the foundation must be laid by also studying information quality, tocontribute to knowledge creation in one important way.

Knowledge conversion processesAccording to Nonaka and Reinmoller (1998), in order to design knowledge-creatingareas, all the processes by which knowledge is converted need to be supported withinthe region. Knowledge conversion in networked innovation processes has beenoriginally focused on by Nonaka and Takeuchi (1995). Their four-phase SECI model ofknowledge conversion has the aim of causing a learning spiral where a collectivelearning process increases knowledge in the network. Knowledge conversion takesplace in certain forums or arenas (ba in Japanese). Ba can be a concrete or virtual placewhere knowledge conversion occurs. Different kind of knowledge processes needdifferent kind of bas. Each phase of the SECI model corresponds to a specific ba:

. Socialisation to originating ba (the sharing of tacit knowledge betweenindividuals through physical proximity and face-to-face contacts; thesocialisation phase in originating ba creates a common understanding andsocial capital among group members).


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. Externalisation to interacting ba (the expression of tacit knowledge and itstranslation into comprehensible forms that can be understood by others; takesplace in interacting ba, where dialogue is the key to knowledge conversion).

. Combination to cyber ba (the conversion of explicit knowledge into more complexsets of explicit knowledge so that new knowledge generated at theexternalisation stage transcends the group in analogue or digital signals;cyber ba represents the combination phase where combining new explicitknowledge with existing information and knowledge generates and systemisesexplicit knowledge).

. Internalisation to exercising ba (the conversion of explicit knowledge into tacitknowledge by embodying explicit knowledge in action and practice by usingsimulations or experiments to trigger learning by doing processes; exercising bafacilitates the conversion of explicit knowledge to tacit knowledge).

For a discussion of the critique concerning the SECI/ba model, see Harmaakorpi andMelkas (2005). The SECI/ba model is designed for organisations having a clearleadership and a hierarchical structure enabling decision-making and control in theknowledge creation process. A regional innovation network, however, lacks a clearleadership, which potentially makes it more difficult for the learning spiral to function.We consider – as does Nonaka himself – the SECI/ba model to be sufficientlyapplicable to regional development and innovation networks. After all, modern firmorganisations that Nonaka and his colleagues investigated, and where knowledge iscreated, are no longer hierarchical but, rather, networked entities.

Research has barely been done on knowledge management systems for looseregional multi-actor networks. These networks have emerged only fairly recently inFinland, which may be one reason for this lack of research here. Smaller and morehomogeneous environments such as companies may be easier to investigate, but firststeps are only taken in wider innovation environments. The few attempts made fail toaddress creation, diffusion and utilisation of future-oriented knowledge as well asdifferences between data, information and knowledge – the basic materials ofknowledge management – in a systematic manner. In addition, we argue thatknowledge assets and knowledge vision are additional elements that need to beincluded in such schemes. Knowledge assets – the inputs, outputs and moderatingfactors of knowledge creation and management – lay the foundation for knowledgecreation. Moreover, the SECI/ba model does not tell how to lead the process. Nonakaet al. (2000) have created the concept of knowledge vision to give a direction to theprocess. To be able to create and manage knowledge successfully, a network needs asynchronising vision. This is especially important in regional multi-actor networks,where actors have very different backgrounds. (For a more detailed discussion, seeUotila et al., 2005.)

A special challenge is also incorporation of self-transcending knowledge into theSECI/ba model that originally only concerned tacit and explicit knowledge. We arguethat this requires taking into account two additional knowledge conversion phases:

(1) the conversion of self-transcending knowledge to tacit knowledge(embodiment); and

(2) vice versa, the conversion of tacit knowledge to self-transcending knowledge.

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Within a regional innovation network, these processes are both collective andindividual. The network, however, needs to facilitate, support and systematise theprocesses – so they need to be included in the knowledge management system. Thefirst-mentioned process may be seen as taking place in “imagination ba” and thesecond in “futurising ba”. We have given these processes the following names toillustrate their nature:

. visualisation (from self-transcending to tacit); self-transcending knowledge isembodied from the abstract to visions, feelings, mental models, etc.; and

. potentialisation (from tacit to self-transcending); tacit knowledge is disembodiedand forms the basis for sensing the future potentials and seeing what does notyet exist.

For a more detailed description of these processes, see Harmaakorpi and Melkas (2005),as well as Uotila et al. (2005). A revisited model of a learning cycle includingself-transcending knowledge – visualisation and potentialisation – as well asknowledge vision and knowledge assets is illustrated in Figure 3. It has the somewhathumoristic but descriptive name of a “rye bread model”[2] (Harmaakorpi and Melkas,2005). The knowledge-creating process reforms the knowledge assets and is steered by

Figure 3.The rye-bread model of

knowledge managementincluding explicit, tacit

and self-transcendingknowledge


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knowledge vision from the centre of the model. Knowledge creation occurs in thedefined bas using the SECI learning spiral and knowledge conversions.

Depending on the network in question – its needs, characteristics and phase ofnetwork development, the practical content of each ba has to be defined at the level ofan individual network. The rye bread model is a follow-up to the RegionalDevelopment Platform Method (for further details, see Harmaakorpi (2006); Pekkarinenand Harmaakorpi (2006)). The method provides a basis for finding potential regionalresource configurations – regional development platforms – and forming innovationnetworks to exploit the potential existing in the platforms. The rye bread model is aconceptual description of how to promote collective learning and innovativeness inthese networks using the existing regional resource base. It can be turned into aconcrete regional tool by including lists of appropriate actions for the different bas.

The case studyThe Lahti regionThe Lahti region has set a goal to be the leading area in practice-based innovationactivities in Finland. The framework of network-facilitating innovation policy has beendeveloped in the region in order to promote innovation activities (see Harmaakorpi andTura, 2006). The Lahti region’s future competitiveness is seen to be greatly dependenton its ability to integrate knowledge to the practice-based innovation processes, due tothe absence of a university and very low research inputs in the region. The yearlyresearch input in 2004 in the Lahti region was only 255 euros per capita compared to1,800 euros in the Helsinki region and 2,530 euros in the Tampere region. This tellssomething of the knowledge-intensity of the region. However, the Lahti region has afavourable logistic setting: it lies only 100 km from two remarkable research centres,Helsinki and Tampere, enabling the relatively easy transfer of scientific knowledge tothe practice-based innovation processes.

The existing resource configurations in a region set the basis for futuredevelopment and, therefore, the Lahti region has conducted an audit of regionalresource-base and conceptualisation of the regional innovation system by the so-calledRegional Development Platform Method (RDPM) (see Harmaakorpi, 2006). In theRDPM, the framework of network-facilitating innovation policy and the practical toolsfor conducting the policy were outlined. The situation in the Lahti region has, indeed,forced it to develop new tools to trigger innovation processes. One aim of the policytools is to search for structural holes between regional practice-oriented knowledgebase and the research-oriented knowledge base found in the region and in thesurrounding research centres; that is, to absorb the existing knowledge to the regionalinnovation processes. The experiences gained in applying the RDPM and inconducting the innovation policy in the region have further emphasised the importanceof enhancing a regional knowledge management system (see, e.g. Harmaakorpi andMelkas, 2005; Uotila et al., 2005; Harmaakorpi, 2006; Uotila et al., 2006; Pekkarinen andHarmaakorpi, 2006).

Knowledge management framework of the regional innovation network at LahtiAccording to the practical experiences in the Lahti region, knowledge does not getabsorbed between two subsystems of the regional innovation system (a knowledgegeneration and diffusion subsystem; and a knowledge application and exploitation

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subsystem) by itself. Learning and knowledge creation are too important questions tobe left to occur spontaneously. Moreover, experiences have shown that potentialinnovating partners in different subsystems might not be able to even begin innovationprocesses, as common rules for communication are lacking (see Uotila et al., 2006).Even in the same technological field, the language in basic research is so different frompractice-based innovation processes that an innovation process could end before it hasstarted, even if the innovation potential in the structural hole is obvious.

The situation is the same between different technological disciplines. The situationis most complicated when there is a desire to span the structural hole between a partnerwith research-oriented knowledge interest in one technological field and a partner withpractice-oriented knowledge interest in another technological field. An illustrativeexample of that is the relationship between basic nanotechnology research andpractical innovation processes in metal industry. The innovation potential is clear, butinnovation processes are inadequate due to lacking ways of communication. Questionsconcerning data, information and knowledge quality are quite difficult but vital in thiskind of an environment.

Following the theoretical discussions in the beginning of the article, the basicfactors to be understood are that a regional innovation process is actually about:absorptive capacity; in a very diversified environment (two subsystems with Mode 2knowledge production, weak ties and structural holes, etc.) setting demands for specialmeasures of knowledge management in the innovation system that take into accountquality issues related to data, information and knowledge. The combination of theseelements is depicted in Figure 4. Without taking into account the above-mentionedfactors, in this kind of an environment – following our metafora on football – “theinformation and knowledge passes” are prone to get too long and inaccurate to reachthe receiver.

In our case, an innovation process would contain absorbing data, information andknowledge in the bas of the rye-bread model including the phases of acquisition,assimilation, transformation and exploitation. The knowledge conversions should aidthese absorption phases to occur in order to enable an actual innovation to take place.Therefore, we have to look at each ba in the absorption phases and consider the data,information and knowledge used in them.

Identification of the necessary quality dimensions in different bas may be done onthe basis of the quality dimensions based framework of Wang and Strong (1996). Theydeveloped a hierarchical framework with four data quality categories and 15dimensions (see also Figure 5):

(1) intrinsic data quality consisting of accuracy, objectivity, believability andreputation;

(2) contextual data quality consisting of value-added, relevancy, timeliness,completeness and appropriate amount of data;

(3) representational data quality consisting of interpretability, ease ofunderstanding, representational consistency and concise representation; and

(4) accessibility data quality consisting of accessibility and access security.

Definitions for all the dimensions are listed in their article. Wang and Strong’sframework has more dimensions than works of other researchers. Most studies have


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Figure 4.The combined frameworkfor knowledgemanagement in regionalinnovation systems

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been based on a small set of common quality attributes (for instance, accuracy only).The framework of Wang and Strong has been utilised and advocated later by, forinstance, Wang et al. (1998), Wang (1998), Huang et al. (1999), Kahn et al. (2002) and Leeet al. (2002).

The necessary quality dimensions depend on the type of data, information andknowledge in question. In practical application of the combined framework shown inFigure 4, a mapping of the types has to be done first (cf. Melkas, 2004). Therefore, a listof quality dimensions is not shown here. The necessary quality dimensions may alsodiffer in practice according to the knowledge conversion in question. One function ofFigure 4 is to show how many things should be taken into account, and howcomplicated this is – yet vital for successful innovation activities.

For instance, imagination ba is about absorbing future-oriented knowledge into theinnovation network. It is basicly a conversion of self-transcending knowledge into tacitknowledge (visualisation). However, the world is full of future-oriented data,information and knowledge. When we try to absorb those successfully, what should betaken into account? Theoretically, a list might look like this:

. in the case of data (such as statistics), at least timeliness, completeness andappropriate amount of data;

. in the case of information (such as results of technology foresight surveys),value-added, relevancy, interpretability, ease of understanding and accessibility;and

. in the case of knowledge (such as tacit knowledge of practitioners): objectivity,believability, value-added, ease of understanding and accessibility.

The above list is an example, and each individual type of data, information andknowledge as well as each conversion phase may require different dimensions to betaken into consideration. Factors affecting the necessary dimensions are also thenetwork, its structure, field of operation and objectives as well as the innovationprocess in question.

There are also differences in how relevant each ba is for absorptive capacity and theprocesses of acquisition, assimilation, transformation and exploitation. In each of theseprocesses, different phases of conversion take place. In innovation activities, the

Figure 5.A hierarchical

representation of dataquality


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conversion phases do not necessarily take place in the order shown in Figure 4. Nonakaand Takeuchi’s (1995) work is based on the view that the conversion phases have anorder, but in practice, there is not necessarily any spiral or any systematic orderwhatsoever. These activities are by nature chaotic. They proceed from highly vagueand fragmented ideas to – at best – a more detailed and clear common understandingof the network, enabling practical action in the form of, for instance, designing a newmobile phone. The exploitation process is thus no longer taken care of by a very diffusenetwork, but in each process, criteria for data, information and knowledge quality areargued to be necessary. In other words, by means of looking into the concepts ofabsorptive capacity and its related processes – bas, conversion phases and qualitydimensions – innovation processes may be aided in practice.

Quality dimensions for the quite different types of knowledge, especially tacit andself-transcending knowledge, are definitely worth further research. Paying attention tothese conversion processes appears to be particularly challenging, given thenecessarily spontaneous and sometimes even chaotic nature of these processes inparticular.

Brokerage functionsA final further issue is the question of brokerage functions. A special feature ofregional innovation networks is the need for “long passes over the playground”. Thedifference of knowledge interests between innovating partners is often so big that aspecial interpretation function is needed. Burt calls this special function “informationbrokerage in the structural hole”. A structural hole is an opportunity to broker the flowof information between people and control the form of cooperation that brings togetherpeople from opposite sides of the hole (Burt, 1997).

This kind of information brokerage or information arbitrage is often done – or atleast should be done – by intermediate organisations of a regional innovation system.These organisations include, for example, regional science and technology parks aswell as business development organisations and technology transfer organisations ofuniversities and research centres. The information brokerage in these organisationscould occur by, for example:

. making people on both sides of a structural hole aware of interests anddifficulties in the other group;

. transferring best practices;

. drawing analogies between groups ostensibly irrelevant to one another; and

. making syntheses of knowledge interests (Burt, 2004).

It is here a question of working at, so to say, many fronts. Emphasis needs to be oncombining:

. loose innovation network development;

. as far as possible, an explicit, systematic approach to planning and working onabsorptive capacity and data, information and knowledge quality matters withinthe network in question; and

. brokerage functions.

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Actual specifications, careful planning and monitoring may not be possible, but somesystematisation can usually be done. Even appointment of a “Chief Information andKnowledge Officer” (cf. “Chief Knowledge Officer” in Nonaka et al., 2001; and“information product manager” in Wang et al., 1998) as an information broker for thenetwork or its subprocess could be considered, depending on resources available. Thisperson would concentrate on planning and monitoring as well as keeping these topicsin the actors’ minds, but very importantly – without making this aim ofsystematisation an end in itself.

Practical tasks for a broker could contain:. definition of the operational logic of the innovation network with regard to data,

information and knowledge;. identification of necessary flows of data, information and knowledge, as well as

potential bottlenecks in these flows;. identification of roles of actors in relation to data, information and knowledge

and consideration of the needs of the different roles (information producers,information custodians, information consumers, information brokers and soforth);

. consideration of strategic versus tactic/operational gains that can be broughtabout by a good level of data, information and knowledge quality; and

. identification of the necessary data, information and knowledge quality fordifferent types of materials, conversion phases and processes.

Discussion and conclusionsThis article investigated:

. special characteristics of regional innovation networks in the context of data,information and knowledge of different types;

. interaction of the latter within regional innovation networks;

. building of a knowledge management framework for a regional innovationnetwork with considerations of quality of data, information and knowledge; and

. aiding absorptive capacity in regional innovation networks by means ofknowledge management.

It thus responded to a need to study matters related to knowledge management anddata, information and knowledge quality in such relatively new environments. A lot ofemphasis has been put recently on regional innovation systems, where various kind ofactors are involved in innovative processes. The approach in the present article was todiscuss regional innovation networks and data, information and knowledge as well asresearch on them at a theoretical level. Thereafter, a knowledge managementframework for a regional innovation network was introduced. The framework has beenbuilt utilising an existing innovation network of the Lahti region, Finland, as a pilotenvironment. The article showed how the framework developed is based onestablished knowledge management literature and practice.

The discussion in the article confirms that – in addition to knowledge – data andinformation quality need to be addressed systematically in regional innovationnetworks. Conventionally, information quality has been described as how accurate


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information is, but in recent years, the view of it has widened considerably. It isintertwined with knowledge management and network management. This is, first,because knowledge controls and guides decision-making and other processes throughassessment of information. Awareness, again – necessary for activities infuture-oriented innovation networks – occurs only after both information andknowledge have been assessed and judged against situations and experience. Second,while it has been found that quality of information cannot be improved independentlyof processes that produced this information and of contexts in which informationconsumers utilise it, the same applies vice versa – contexts and processes of regionalinnovation networks, for instance, cannot be improved independently of quality ofinformation – and of data and knowledge.

The knowledge management framework described in this article incorporates, apartfrom information quality considerations, also future-oriented self-transcendingknowledge as well as knowledge vision and knowledge assets. The framework isbased on Nonaka and Takeuchi’s (1995) work. They set their focus on “knowledgeconversion” in networked innovation processes. Traditionally, knowledgemanagement programmes and considerations have not been combined withconsiderations of the hierarchy of data, information and knowledge as well as theirquality. It seems essential that knowledge management programmes of the future arecombined with management of such quality – otherwise their foundation remainsincomplete. The view of information quality in this article is mainly based on theso-called quality dimensions based approach advocated by, inter alia, Wang andStrong (1996).

According to the practical experiences in the Lahti region, knowledge does not getabsorbed between the two subsystems of the regional innovation system (generationand diffusion; application and exploitation) by itself. Following the theoreticaldiscussions in the beginning of the article, the basic factors to be understood are that aregional innovation process is actually about: absorptive capacity in a very diversifiedenvironment (two sub-systems with Mode 2 knowledge production, weak ties andstructural holes, etc.) setting demands for special measures of knowledge managementin the innovation system that take into account quality issues related to data,information and knowledge. Without taking into account the above-mentioned factors,in this kind of an environment – following our metafora on football – “the informationand knowledge passes” are prone to get too long and inaccurate to reach the receiver.

However, a special feature of regional innovation networks is the need for these longpasses over the playground. The difference in knowledge interests between theinnovating partners is often so big that a special interpretation function is needed –“information brokerage in the structural hole”. This is often done – or at least shouldbe done – by intermediate organisations of the regional innovation system, such asregional science and technology parks as well as business development organisationsand technology transfer organisations of universities and research centres.

Emphasis in regional innovation networks needs to be on combining, on the onehand, loose innovation network development and, on the other hand, an explicit,systematic approach to planning and working on data, information andknowledge-related matters within the network. Specifications, careful planning andmonitoring may not necessarily be possible, but some systematisation is vital inaddition to innovative freedom. Even appointment of a “chief information and

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knowledge officer” for the network or its sub-process could be considered. This personwould concentrate on planning and monitoring as well as keeping these topics in theactors’ minds – but very importantly, without making this systematic approach anend in itself in the innovation environment.

Limitations of the knowledge management framework developed will be assessed infuture studies. The present article combines in a novel way research fields that havepreviously barely been combined – data, information and knowledge quality; knowledgemanagement; and regional innovation networks. By doing this, it provides new insightsinto a societally important theme and shows possible avenues of further research.Innovation-related activities are not routine tasks, and it is therefore particularlyimportant for them to be able to benefit from a comprehensive and good quality basis.

Notes

1. For reasons of clarity, information is used here instead of listing data, information andknowledge each time. Information can be claimed to be most commonly the basic material ofcommunication.

2. The name was given to the first drafts of the model that looked very much like a traditionalFinnish rye bread. The descriptive name has so far been retained despite minor changes inthe layout of the model.

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About the authorsHelina Melkas, DSc (Tech), is Senior Researcher at Lappeenranta University of Technology,Lahti Unit (Lahti, Finland). She has worked also at Helsinki University of Technology,International Labour Office (Geneva), United Nations University/WIDER (Helsinki) and theFinnish Ministry of Labour. Her research interests are related to information quality, knowledgemanagement, social networks and well-being technology. Helina Melkas is the correspondingauthor and can be contacted at: [email protected]

Vesa Harmaakorpi, DSc (Tech), is Professor of Innovation Systems at LappeenrantaUniversity of Technology, Lahti Unit (Lahti, Finland). Professor Harmaakorpi has hisbackground in business life. The last eight years he has worked within the universitycommunity, e.g. as deputy director and director at Helsinki University of Technology, LahtiCentre. His research interests are innovation systems and processes as well as innovationenvironments linked to regional development. Vesa Harmaakorpi can be contacted at:[email protected]

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