Innovation and Knowledge Management for Sustainability: Theoretical Perspectives
Frisian Academy (KNAW), The Netherlands & University of Groningen, The Netherlands
Frisian Academy (KNAW), The Netherlands & University of Groningen, The Netherlands
This chapter supports the argument that innovation is a special case of knowledge management; it is about knowledge creation. With economic profit as its driving force, innovation is mostly short term and commercial, feeding the question whether innovation really can be applied to ecological and social systems. The problem concerns the goal of innovation: what does it suppose to realize? In this chapter, we combine knowledge management (KM) and innovation concepts with sustainability and we argue that as long as the emphasis in innovation is on “profit” and not on “people” and “planet” (the three P’s of sustainability) we have no guiding mechanism for innovation, namely the existence of a sustainable future. In a sustainable perspective, innovation becomes an instrument that benefits society at large. In this chapter, we explore concepts behind issues of KM and innovation through literature review and we argue along three lines of thinking. First, we demonstrate that innovation is knowledge creation at an individual and collective level. Second, we argue that innovation should be a means and not a goal. Third, we offer a perspective to operationalize the relationship between knowledge, innovation and sustainability. Sustainability as an issue requires adaptation of human and social systems to ever-changing environments. This continuous need for change demands people to constantly develop and obtain new knowledge to realize the balance between system and environment. We conclude this chapter by introducing concepts on Knowledge of Sustainability (KoS) and Sustainability of Knowledge (SoK) that form the synthesis of our discussion, and we set the outline of a framework for sustainable innovation.
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The debate about the value of innovation is not new. Beginning in the 80s and getting stronger in the 90s, the EU has continuously stimulated innovation. One might say, metaphorically, that innovation looks like a medicine that can cure almost all social, technical and economic ailments. (Kleinknecht, 1990; Nooteboom, 2003; 2008). Of course, continuous innovation is an illusion. Improvement is often possible, but realizing something new often also brings along “destruction” as we know from Schumpeter (1934). We therefore make a distinction in innovation as improvement and innovation as an ideology. When one argues that conservatism is the general mentality, or that our economic climate is too weak or that the entrepreneurial spirit has to be stimulated and then concludes that we have to put more effort in innovation, innovation is used as an ideology. Innovation as improvement is different, but has the assumption that one has goals in mind or knows the present shortcomings. We then have to answer questions like “which goals?” or “why changes and therefore destruction?” or “is innovation a common remedy?”
It should be obvious that an answer to the abovementioned questions cannot be given by one slogan. Innovation lends itself to a multitude of meanings. Often innovation is realized with the sole purpose of propelling economic growth. A broader orientation that looks at long term changes, for example with respect to ecological and social sustainability is missing if one focuses on this short term economic growth. This opens the debate to (1) mindlessness in innovation regarding its consequences, and (2) sustainability as an “answer” to address these consequences. The present basic attitude is primarily a profit orientation in which innovation leads to the development of new products, processes and services, and if they are available (supply), there will be a demand. Therefore, economic activity will increase and so will growth, which is - as economists argue - favorable for us all. On the other hand, innovation is initially nearly always a continuation of the (successful) past. It does not come out of “the blue” and very often innovation destroys existing products and services and in this way seems to decrease economic strength (“creative destruction”). It seems that everyone knows what innovation is and that it simply has to be realized. We believe that this perception is at least incomplete, especially if one looks at innovation from a knowledge management and sustainability angle.
In this chapter, innovation will be considered as a special phase of knowledge management, or to be more precise, innovation is about knowledge creation and knowledge acceptance. Although we need a general definition of innovation, we will not dig into the many definitions of innovation. Many adhere to an interpretation of innovation as an instrument that leads to more (economic) growth. That is not our focus. As far as innovation is concerned, we want to show that the realization of “something new” can or perhaps should be related to an increasingly important issue that is relevant for humankind in general. We are talking about sustainability, the dynamic balance of a system - of whatever kind - and its environment. This complicated dynamic balance is not beyond innovation, it is the kernel of innovation and in realizing this balance we have to create knowledge. We have to innovate in order to make our social and natural system sustainable, not to make it endlessly grow. To our knowledge, no existing physical or biological system can grow endlessly. Utterly important for any discussion about innovation are two things: the presence of knowledge (of sustainability) and the creation of (new and sustainable) knowledge. One has to know facts, rules, practice and theories on the one hand. On the other hand, one must have the conviction that risks and uncertainties, which are implicit in any innovative activity, will be such that “something better” is realized. We will argue that the issue of sustainability requires a perspective on innovation that shifts the attention from “profit” and “planet” to “people” (Elkington, 1997; 1999). The only way in realizing this is by using knowledge management.
The structure of this chapter is as follows. In the section “Knowledge and KM”, we will discuss various aspects of knowledge management (KM): the functions, the phases, the levels of aggregation and the various generations of KM. In the section “Innovation as Knowledge Creation”, we will especially focus on 2nd generation KM in the sense of knowledge creation (innovation). In the section “Sustainability (the Knowledge of)” and in the section “From KoS tot SoK”, we will make the step towards sustainability by introducing the notion of “knowledge of sustainability” and we will explain what we mean by this concept. Furthermore, knowledge of sustainability has to be operational and that is why we also introduce another term: “sustainability of knowledge”. The relation between the various concepts will be depicted in Figure 2 section. In the last section, we will give conclusions.
The first question in discussing KM is to answer “what is knowledge?” Then confusion starts. Do we still have to define what is already known in philosophy and epistemology for more than two thousand years? The answer to this question is affirmative; because how can we otherwise manage (control, coordinate or direct) this something called “knowledge”.
We will not address the age-old discussions about knowledge. However, an excellent entrance can be found in The Encyclopedia of Philosophy (EoP; Edwards, 1967). According to the EoP: “knowledge is justified true belief”. In a more elaborated formulation, knowledge can be approached as a belief on which one has very strong grounds for thinking it is true and that it has to be justified, eventually, whether it is of the propositional or inferential kind. It is interesting to see that this EoP reference to “knowledge” is missing in most present-day’s discussions in KM. One characteristic determination of knowledge, is that - whether it is inter-individual (inter - subjective) or supra-individual (Plato’s World of Forms) - it is always connected to cognition of individuals. This implies that human beings are always involved as interpretation mechanisms. From a human information-processing point of view (Newell & Simon, 1972), knowledge is interpreted information. We explain this position later after we have given a short history of KM.
Until recently, knowledge management was not an issue in management science. Management is supposed to be about the control, the regulation and the command of things. However, “knowledge” only became fashionable some twenty years ago. There is a lot of debate about the meaning of KM. We believe that knowledge (management) can have three meanings (Dalkir, 2005; Jorna, 2007). First, knowledge is a fashionable, somewhat more mysterious word for information, implying that knowledge management is the same as information management. Second, knowledge management is about the assessment of all kinds of competencies of staff in organizations, in which case it is almost similar to (sophisticated) Human Resource Management (HRM). Third, knowledge management is about managing the form and content of ideas, thoughts and actions of human individuals. In the remainder of this chapter we are not favoring the information equals knowledge or the HRM interpretation. Our interpretation of KM is the focus on ideas, thoughts and actions of humans, individually and collectively. We will start with discussing various aspects ofknowledge and KM.
First, we argue that data, information and knowledge are parts of a three-stage rocket (Jorna & Simons, 1992; Schreiber, et al., 2000; Jorna,
2007) . Knowledge is based on information, and in turn information on data. Data are the noises, scratches, images and other unstructured elements from reality. If data are interpreted explicitly, we speak of information. If information is used in reasoning or in performing actions by people - i. e., if it is interpreted - the result is knowledge. This line of thought, namely that knowledge involves reasoning and therefore making adaptations or interpretations on data and information, also means that in the chain from data to knowledge the degrees of freedom vary. Data can be interpreted in many different ways to serve as information. In a similar way, information can be interpreted in many different ways to serve as knowledge. The crucial distinction in information and knowledge is interpretation. This activity is carried out by humans as information processing systems. Humans are the ultimate carriers of knowledge and therefore also of innovation (Jorna, 2006).
Second, when knowledge management is conceived of as information management, the assumption is that knowledge more or less equals information. Information is available, accessible and can be shared and utilized. For that reason, the relationship with information and communication technology (ICT) is almost a natural one. With the help of databases, decision support systems and other ICT-tools, sharing, storing and using information is evident. The assumption with regard to knowledge is that it is a kind of information. However, problems arise when issues of innovation and creation appear. Then the equivalence between information and knowledge stops. It is quite natural to talk about knowledge creation and knowledge acquisition, but it is strange to refer to information creation or information acquisition. To formulate it stronger, ICT cannot in itself create or acquire knowledge. These processes have to be performed by humans. Therefore, the orientation towards knowledge creation and acquisition, as very important activities and processes in organizations, shifted the attention away from the ICT-orientation of KM in which knowledge management is information management. In the section “Innovation as Knowledge Creation”, we will argue that most discussions about innovation neglect or ignore the essential knowledge creation process by humans (Boden, 1994a).
KM started as a discipline with regard to organizational issues of knowledge. An organization or company has to deal with storage, sharing, distribution or use of knowledge. Although every researcher in KM knows, that staff is the ultimate quality or “gold” in the performance of an organization, this individual perspective remains mostly implicit in standard innovation models (Nonaka & Takeuchi, 1995; van der Ven et al., 1999).
Another issue in KM is that the development, fluctuation or dynamics of knowledge, and therefore implicitly of learning, often is not accounted for. Knowledge seems to be a static, stable and defined entity, which can be manipulated and controlled as if it were something material. Nothing is less true and it seems that common sense in KM takes the volatile or dynamic character of knowledge for granted. This is unsatisfactory. The only theoretical research that at least deals with the dynamics of knowledge has been formulated by Boisot (1995). He offers a model in which change and dynamics ofknowledge are accounted for. We come back to the dynamics issue in the third section. We first want to explain the functions in organizations that KM is suitable for. In spite of all the attention for innovation, it should be kept in mind that innovation in the sense of deep exploration (March, 1991) - or second order learning (Argyris & Schon, 1978) - is not the usual practice in many organizations. One could argue “it better be so”, because being only innovative all the time is non-human or at least unrealistic.
Functions of knowledge management activities are use, sharing, distribution, development and integration of knowledge elements within an organizational context. These different activities operate on different levels (Dalkir, 2005). Knowledge use points to the usual business processes of staff, focusing on the actual application of a person’s knowledge during normal task executions and processes. A person’s existing set of knowledge elements enables him to make certain decisions and to perform his task.
Both the activities of knowledge sharing and knowledge distribution take place at the level of a group of co-workers, expressing the transport of knowledge between them. The distinction between these two functions concerns the mode of communication. Knowledge sharing refers to peer-to-peer knowledge transfer where knowledge elements of one person are transmitted to another who integrates it into his own set of knowledge elements. In knowledge distribution, knowledge elements of one person are transported to multiple colleagues.
Knowledge integration refers to the acquisition of knowledge elements from sources that are external to the individual, focusing on the input-side of employees. From external sources, a member of an organization is presented with “new” knowledge elements. Subsequently, he integrates the knowledge elements into his own set.
Knowledge development concerns the generation of new knowledge elements. The generation of new knowledge elements is mainly for most an internal (mental) activity of a person. Based on an existing set of knowledge elements, a person creates new knowledge elements. Knowledge development and creation primarily are individual human-information processing activities.
As indicated earlier, innovations are based on knowledge; they have knowledge as input, as throughput and as output (see section “Innovation as Knowledge Creation” for details). In KM literature (Dalkir, 2005; Jorna, 2006) various phases in dealing with knowledge are distinguished. The phases are knowledge creation, encoding, storage, sharing, maintenance and use.
Although in Figure 1 the subsequent phases are depicted linearly, they in fact take place iteratively. Knowledge creation itself already starts with something. An actual “creatio ex nihilo” (creation from nothing) does not occur, not even in radical innovations. Within this sequence, the content of knowledge can be looked at phase by phase, with regard to the people involved, the form or type of knowledge expressed and the importance of the individual or team, respectively. The phases of creation and encoding are essential in any discussion about innovation. In the remainder of this chapter, we will discuss knowledge content only when it regards the issue of sustainability. Knowledge content is too specific and domain dependent to be discussed in general. However, we will discuss details of the types of knowledge for two reasons. In the first place because in the various phases of innovation the same type can not be dominant and in the second place because the dynamics of knowledge types is as relevant as the dynamics of knowledge content and is the motor of knowledge creation.
The relation between knowledge and innovation is simple. Knowledge is both the primary source and the outcome of an innovation. It is the input, throughput and output of innovations. Apart from creating knowledge, the key activity in innovation is making and sharing knowledge. No matter how brilliant a new product or service is, if it is not transferred or exchanged, the innovation will not be successful.
To complete our picture of innovation from the knowledge perspective, a distinction can be made in content of knowledge and how this knowledge content is expressed: its form or type. Content ofknowledge refers to domains, e. g., the construction of houses, physics, the working of
computers or health care. Knowledge according to type is the denomination of how this knowledge content is presented. The various aspects of knowledge make it almost impossible to define types of knowledge unambiguously. Based on the work of Boisot (1995), we developed three types of (semiotically inspired) knowledge: (a) sensory or tacit, (b) (en)coded, and (c) theoretical (or tacit) knowledge (Jorna, 2006).
The first type concerns sensory (or tacit) knowledge or just behavior. It starts from a perception of difference, interpreted in terms of an analogy. The situation is well known: when you eat a fruit you never ate before, your reaction to the new taste will be something like: “Well, it reminds me of...” and you name a fruit you know. Essential is to always recognize the situation in terms of a situation you already know. It should be clear that the bigger the sensory “problem” is, the more difficult to find an analogue. We believe that sensitiveness in sensory knowledge is often underestimated but of the utmost importance, especially in the first phases of an innovation. We hypothesize that creative people also are the ones with a big talent for expressing sensory knowledge. As we will argue later, this type of knowledge requires the physical co-presence of individuals and the use of drawings or pictures. The emphasis is not on documents and formulas.
Figure 1. Phases in knowledge management
The sensory - or as some would call it the tacit - perspective underlies what Michael Polanyi has coined “personal knowledge” (Polanyi, 1967, p. 11). He describes the process involved in this knowledge type as being “aware of that from which we are attending to another thing, in the appearance of that thing”. Sensory knowledge is bodily knowledge: “when we make a thing function as the proximal term of tacit knowing, we incorporate it in our body - or extend our body to include it - so that we come to dwell in it” (16). Boisot identifies this knowledge as the domain of the “ineffable” (Boisot, 1995, p. 62). It cannot be coded, it is about concrete experiences, and it can be shared only with those who are physically co-present.
Quantification of sensory knowledge is possible through looking at details. The more detailed a sensory experience is, the richer it is. Knowledge of details is relative to domains. A professional will be able to perceive more when looking at a certain activity than an amateur will. Sensory knowledge can therefore mostly be measured through the analysis of behavior.
The second type, (en)coded knowledge, materializes when signs become codes. Certain aspects of remembered situations (visual, acoustic and tactile forms) evoke these situations. For example, concrete cows are replaced (represented) by the sound “cow” and the category of “cow” emerges. With the sign, codes emerge - a code being nothing else than a convention establishing a relation of substitution. The sign enables communication and makes communication easier. The diffusion of knowledge becomes easier where signs (codes) are available (Boisot, 1995). Externalization requires coding. In terms of Boisot (1995): the diffusion of the sign now takes place along the lines of a social community. Co-workers or partners do not have to be co-present. It is therefore extremely
unlikely that coded knowledge is dominant in the early phases of innovations.
Codes can be quantified by taking into account the number of elements and the combination rules a code consists of. Musical notation systems are more strongly coded (allow less ambiguity) than natural languages. Therefore, in the use of images and metaphors, coded knowledge comes closest to the non-coded sensory knowledge. Further details on the weakness or strength of codes can be found in Goodman (1968), who uses five syntactic and semantic requirements to distinguish weaker from stronger sets of signs (see Jorna, 1990; 2006).
A third kind of knowledge type emerges when a third aspect is added to the aspects of sensory difference and codification (substitution), that of structure or pattern. It arises when coded signs relate to the events represented, not based on a convention, but based on patterned or structural qualities. We then have theoretical knowledge. Scientific, ideological and religious knowledge are of this type. Can one be creative in this phase? Certainly one can learn. However, no longer is knowledge acquired through searching for perceptual analogies or categorizing. Knowledge is now the result of (scientific) inquiry - empirical as well as theoretical. This means that innovation, here, is much more difficult, because an enormous accumulation of past knowledge has to be re-interpreted.
An attempt to quantify theoretical knowledge is to describe this type in terms of “why” or “because” chains. The longer the chain, the more abstract the theoretical knowledge. Therefore, we believe that knowledge creation and therefore innovation at the start is more sensory than theoretical. One should note that not only scientific knowledge is theoretical. Ideologies or religions also provide complex “why” chains and therefore are theoretical too.
The various types ofknowledge are relevant in various phases or functions of KM. Distribution and sharing are not possible without codes and neither is storage. The use of knowledge regards all three knowledge types, of course depending on content. As we will argue later, knowledge creation requires a mixture of knowledge types.
The early propagators of KM were not much interested in innovation or knowledge creation. That has changed over the last 10 years. To make this shifting position clear, McElroy (2003; 2008) made a distinction in 1st and 2nd generation KM. First generation KM focuses on capturing, encoding, storing, sharing and distributing knowledge. These knowledge processes provide knowledge workers with valuable knowledge and are called supply-side knowledge processes. This way of practicing KM emerged when researchers and practitioners assigned the success of flourishing companies to their knowledge processing capabilities. The rise of computers boosted this form of knowledge management even more. It is therefore quite understandable that ICT and KM are often named in the same breath. The purpose of 1st generation KM is to enhance the deployment of knowledge (McElroy, 2003), the exploitation of knowledge (Jorna, 2006) or the utility of knowledge (Boisot & MacMillan, 2004). KM in this form is far from processes like knowledge creation.
This observation raises some interesting questions. Where can the origin ofknowledge that one wants to exploit be found? How is this knowledge created? Who is responsible for validating, judging and criticizing the created knowledge? First generation KM cannot provide answers to these questions (Firestone and McElroy, 2003). Moreover, it just assumes that valuable knowledge has already been created in an organization. First generation KM neglects the fact that valuable knowledge has to be created.
In line with this observation, Boisot and MacMillan (2004) argue that a practical foundation of knowledge management in the 80s and 90s, rather than a theoretical/scientific one, had the consequence that until now “practitioners ofknowledge management have not been much troubled by epistemological or foundational issues” (Boisot & MacMillan, 2004, p. 22). In other words, the primary concern of organizations and knowledge management has been with the economic utility of knowledge. To make an outing to sustainability, the focus is on the P from “profit” and not on the P’s from “planet” and “people”.
Second generation KM acknowledges the fact that knowledge is created, interpreted and evaluated (Firestone and McElroy, 2003). This kind of KM is little ICT oriented and requires a deeper understanding ofthe sources ofknowledge and knowledge processing. The two generations of knowledge management differ on two major aspects: (1) assumptions about knowledge, and (2) appropriate solutions. First generation knowledge management holds a “logistics” perspective on knowledge. Knowledge exists and can be transported and stored. Knowledge needs to be delivered at the right time, at the right place, in the right form, and in the right quality (Schreiber et al., 2000). McElroy labels the logistics view, supply oriented knowledge management. When the presence of knowledge is the focus, solutions are oriented towards delivery. The strong focus of 1st generation KM on ICT is therefore not surprising.
Second-generation knowledge management challenges 1st generation knowledge management on both aspects. First, knowledge is not assumed to exist already. Second-generation knowledge management starts from the notion that knowledge needs to be produced. Knowledge production is explicitly identified as an important knowledge process in addition to knowledge integration. The production orientation strongly builds on a human originating social perspective, which counteracts the strong ICT orientation of 1st generation knowledge management. Second-generation KM perceives knowledge production as a pure human oriented and social activity, which may be supported by technology. Central processes in knowledge production are individual and group learning, knowledge acquisition, knowledge claim formulation, and knowledge claim evaluation (McElroy, 2003; Peters et al., 2010). The various knowledge content and the various knowledge types have a different emphasis within the innovation stages (creation versus implementation), the innovation types (from radical via incremental to imitative) and the kinds of innovation (product, process or organization). We expect sensory knowledge to be more important in radical innovation types and in creation. At the front of new knowledge (innovation or knowledge creation), the emphasis is more on showing and demonstrating and therefore on physical co-presence. The (en)coded and theoretical knowledge could be more important in incremental innovations and implementation. Knowledge management in this sense ensures that processes of knowledge production and knowledge integration are ongoing. The outcomes of knowledge production are to be evaluated knowledge claims (Peters, 2011). We therefore argue in the next section that innovation is knowledge creation and a part of KM.
In this section, we focus on innovation as a special case of knowledge management (see also Tidd, 2006). The stages of innovation are related to the processes ofknowledge creation (production) and knowledge integration (Dalkir, 2005; McElroy, 2003). In the process of knowledge production, new knowledge is “produced” by one or more individuals. During the process of knowledge integration, newly created knowledge is communicated to “others” and integrated into existing knowledge. When observing innovation processes from the perspective of knowledge management, the two (main) phases are: a phase of invention or creation or invention and a phase of implementation (Boden, 1994a; Nonaka & Tackeuchi, 1995). During the phase of invention or creation an individual - and in some cases a group of individuals - conceives something new: a new product, a new process or a new service. The actual process of creation is often considered as taking place at the individual level, but what exactly creation is, is difficult to define. Boden (1994b), for example, uses the definition of creativity as ‘bringing something into existence’ or ‘making something out of nothing’. Csikszentmihaly (1996) states that creativity is a process whereby a symbolic domain within a culture is changed.
Creativity, or “invention” as it is often called in organizational environments, can be studied from the perspective of its behavioral manifestations and of its underlying mental processes and mechanisms (Michon, Jackson & Jorna, 2003). Examples of the former are a comparison of the paintings of Magritte and Rembrandt or a comparison of the Operating Systems MS-Dos (now Windows 7) developed by Bill Gates and Mac - OS by Steve Jobs. Results of these analyses may consist of lists of characteristics explaining that for the software case Mac-OS is more creative than MS-Dos. Very often lists of characteristics in the software case are interpreted in terms of styles ofworking, interfaces, functionality or even related to personality typologies suggesting that, for instance, Steve Jobs as a person is more open, innovative or inspiring than Bill Gates. Similar lists, but different in content, can be developed for the comparison of all kinds of new products, services and processes. Practice shows that the decision as to what product or service is more creative cannot be reached easily.
More interesting with respect to creation from an individual, cognitive point of view are descriptions of individual cognitive processes and mechanisms. This concerns individuals in the creativity phase, but very often also the interaction of groups in the implementation phase. Concerning individual acts of creation, the most elaborate attempt to deal with creativity within an overall cognitive, human information processing, framework has been undertaken by Boden (1994a, 1994b). Boden uses a computational view on creativity, which also implies the issue whether and how creativity can be implemented in artificial systems. Boden gives four phases of creativity, earlier proposed by Poincare: preparation, incubation, inspiration and verification. In Boden’s cognitive perspective, this is translated into the framework of symbols, representations and the manipulations on symbols within the human mind. Because creativity implies thinking and problem solving, it means that problem spaces or conceptual spaces are the starting point for every systematic and scientific discussion about creativity. It is, however, difficult to look at and collect empirical data, because the start and ongoing of creative mental processes and creative products are unpredictable and surprising and therefore difficult to observe. One cannot just sit and wait until a creative idea materializes.
With respect to creation, two kinds of novelty are discerned, the first consisting of novel combinations of familiar ideas, the second consisting of the transformation of initial conceptual spaces themselves. The former implies that combinations are found that are improbable, the latter that combinations are formed that are impossible. The last kind of novelty is often understood as “creativity”. It is a bisociation of matrices (Koestler, 1964). These matrices are in fact the mental representations in a human cognitive system. They may consist of stories, icons, texts, images or schemas, but they form the basis upon which changes take place. Bisociation means that common structures or common combinations are dissolved and that new combinations in the human mind arise by processes such as analogy, metaphor and so on. In this sense, being familiar with and bypassing constraints lie at the heart of creativity. Removing constraints then may result in new ideas and new thoughts. It should be acknowledged that Boden’s theory says nothing about the content of a creative product or idea, it is only about unraveling the mental processes and mechanisms in, especially, the creative or invention phase.
During the implementation phase, the idea that was conceived in the invention phase is transformed into the final innovation outcome. For instance, products are developed and implemented, services are formalized and people are trained. In the implementation phase “important others” have to be convinced and be made part of the novel product or service. This is a process of one individual interacting with many others. Here, the sociological aspect will dominate the psychological perspective. In Table 1 we depict the individual and group level for the various phases of KM in combination with innovation. Depending on the characteristics and the stages of innovation and whether an innovation is product, process or organization oriented, knowledge will play a different role, individually or collectively. In the very early phases, the individual is of utmost importance, whereas the acceptance of an innovation and its use in practice can only be done by groups in companies or in society. However, if it concerns organizational innovations, the group relevance is present from the very beginning.
There is a big problem in determining the relevance of innovation. Often innovation is only positioned in terms of its neo-liberal economic valuation. As a consequence of this dominance, it is not only difficult to reject this orientation, it also requires a change of mindset, for example, if one looks at sustainability that regards the whole world population. Now, the dominant view on innovation is the “profit” perspective. An innovation is successful if it has technological and commercial success. If an innovation contributes to (economic or financial) growth, it is relevant. Even if it contributes to “planet” and “people”, it is very difficult to determine or measure this valuation and therefore one often returns to the financial measure of “profit”. Even the “Millennium Development” (MD) Goals of the United Nations are difficult to incorporate in the people perspective. The MD goals are about ecological, human and social issues, but they have various shortcomings. They are not about innovation and knowledge creation, they only marginally integrate private firms and organizations, they work very much top-down (from nations to people), and not as we prefer bottom-up (from people to nations), and they are very difficult to quantify, except for basic economic needs (UNMD, 2000, p. 5). Following this direction we are back at the profit issue.
The before mentioned focus in innovation on profit is like a snake biting into its own tail. This is another way of formulating the innovation paradox. If an innovation is not commercial, it is not successful and if it is not successful or commercial, it is not an innovation. The question is whether there is a way out. We argue that dealing with sustainability is required, because in contrast to what many neo-liberal economists know, but do not believe: without “people” or “planet” there is no “profit”.
One could argue that “social innovation” fulfils the needs ofthe “people” part in the sustainability discussion (Taylor, 1970; Mulgan, 2006). We have two reasons to note differences. In the first place social innovation is about the content of innovation, comparable to product or service innovation. This is not our primary concern. In the second place the sustainability issue with regard to “people” always requires or implicitly assumes a criterion or threshold. This is not in general the case with “social innovation”.
In the next two sections, we will therefore focus on sustainability. Can the issue of sustainability be attacked from an innovation point of view? We believe it can, but only if one uses a knowledge management perspective and not a “profit” perspective only. Sustainability depends on knowledge, but social sustainability also requires joint knowledge actions.
Table 1. The hypothesized presence or absence of group and individual in knowledge phases
The previous sections addressed the topic of KM, and the position innovation holds within. Central to our discussion has been knowledge as basic ingredient of both KM and innovation. Various aspects of knowledge have been discussed, such as the types of knowledge that are identified. What lacked from our discussion so far however, is substance; until now, knowledge has been described without reference to a particular instance or domain. What we mean, is that knowledge was discussed without discussing its content. Knowledge always has some content that is linked to a specific domain. Schreiber et al. (2000) refer to knowledge elements as the elementary building blocks and a knowledge domain as a coherent set of such knowledge elements that together comprise a specific domain of interest. In this way, one can speak of “quantum physics” or “gardening” to refer to a specific body of knowledge.
Similar to the examples provided, the topic of sustainability denotes such a knowledge domain consisting of numerous knowledge elements. Sustainability, in contrast to for instance gardening, is not an easily described knowledge domain. Various interpretations of sustainability have been provided since the term was first used in the 1960’s in the context of dealing with environmental problems. A commonly used definition of sustainability stems from the World Commission of Environment and Development (WCED), which describes a sustainable development as “a development that meets the needs of the present without compromising the ability of future generations to meet their own needs” (WCED, 1987, p. 43). Although commonly used, this definition does not provide any footholds or further elaborations of what incorporates the knowledge domain of sustainability. Aiming to provide more footholds for businesses, Elkington (1997; 1999) uses the Triple Bottom Line to capture the domain of sustainability and provide footholds for organizations to deal with it. He describes sustainability in terms of “people”, “planet”, and “profit”. In each action of an organization, all these elements should be considered; more importantly, there should be a balance between these three elements. Elkington argues that only when inclusiveness and balance are addressed, an organization will be sustainable. However, how these criteria should be met remains to be seen.
In search of an answer to the question what sustainability is about, Faber et al. (2005) found about 3 0 definitions of sustainability and over 300 indicator lists and practical initiatives including reports, guidelines, and policies. Although commonalities exist, each definition and initiative provides a specific perspective on sustainability, either having a broad and shallow, or a small and detailed scope on issues of sustainability. In their analysis, Faber et al. (2005) use a systematic framework to position found definitions and operationalizations. Here, we discuss the framework briefly to illustrate the complexity that is involved in dealing with the domain of sustainability. The framework is constructed using a system perspective on sustainability. The argument is that sustainability is a property that is ascribed to artificial systems. We discuss the implication of applying an artificial system perspective in the
next section; for now we use the term system. When attributing sustainability to a system, this expresses the existence of a balance between the system and its environment, such that the system can exist forever.
Faber et al.’s framework for analyzing the various interpretations of sustainability consisted of three dimensions that form a so-called sustainability space. The first dimension indicates the tangibility ofthe system that is dealt with, stretching from concrete (e. g., a car) to abstract (e. g., an organization). The second dimension deals with the goal that is considered in ascribing the sustainability property to the system, stretching from absolute to relative. An absolute goal orientation builds on the belief that a specific configuration of the system exists that is sustainable under all conditions. The relative goal orientation rej ects the absolute stance, expressing the need to constantly monitor changes in the system’s environment and adapt the system to these changes to restore the balance. The third dimension expresses the interactions between the system and its environment. This dimension relates to the question whether changes in composing parts and internal structures of the system and its environment are taken into consideration in the sustainability property. Interactions are either static or dynamic. At the static end, the environment is considered to be static. Interactions between system and environment therefore do not change over time. At the dynamic end, changes in composition and structure of environment are considered. At the static end of this dimension only the magnitude of interactions is relevant. At the dynamic end not only the magnitude of interactions, but also the quality and the dynamic changes of interactions need to be considered (Faber et al., 2005).
In relation to the present discussion, Faber et al.’s (2005) framework shows that in the knowledge domain of sustainability multiple layers of complexity are detectable. When observing the extreme corners of the sustainability space we notice the following. In its simplest form, sustainability concerns only concrete systems. For these systems clear sustainability criteria can be formulated. Finally, as these systems operate in stable environments, their sustainability will not be affected by unforeseen changes. The complex corner ofthe sustainability space is occupied by an entire different species of systems. There, abstract systems reside, for which no absolute measures of sustainability are available. Even stronger, each measure that exists is susceptible to change due to continuous changes in the system’s environment, constantly affecting system-environment interactions.
These two sides of sustainability put entirely different demands on anyone dealing with sustainability, particularly in relation to the knowledge s/he needs in order to grasp the domain, and all the more in starting or considering innovations of whatever kind. In the simple corner of the sustainability space, problems once encountered and solved, provide the knowledge to deal with all future problems. As indicated, these systems are concrete as well as their composing parts. The environment of these systems is stable, and a finite state of sustainability is assumed to exist. Therefore, the objective is to control the system’s behavior such that this state is maintained. Once one knows what this state of sustainability is and how it can be achieved, no new knowledge is needed to realize and maintain the system’s sustainability. However, in the complex corner of the sustainability space, systems are abstract, residing in a dynamically changing environment, which constantly demands new arrangements of the system, and a finite state of sustainability is not assumed to exist. In this corner, new knowledge is constantly required in order to understand the configuration of the system and the interactions with its environment. Also, the demand for new knowledge regarding the balance between system and environment is continuous. In other words, the knowledge domain in the simple corner of the sustainability space is clearly demarcated and the internal structure is stable. In the complex corner, the knowledge domain is under constant revision regarding knowledge elements and their interrelations, and borders move continuously. It can be discussed what the discussion means with regard to the dominance of the various knowledge types in the sustainability space, but that is not the focus of the chapter at this moment.
Because its underlying assumptions are more realistic (see Faber et al., 2005), we argue that any serious discussion on the sustainability knowledge domain resides in the complex corner of the sustainability space. This implies that sustainability problems and their solutions are by definition dynamic, multi-dimensional, and require in-depth knowledge about cause-effect and process relationships. Therefore, to add an economic perspective, they are beyond “profit” or “growth”. Additionally, an intrinsic demand for the continuous generation of new knowledge is embedded. Hence, appropriate KM, which is equipped to facilitate the knowledge processing demands of this domain, is needed. In the next section, we propose a configuration of KM that facilitates such knowledge processing.
The previous section added content to our discussion on KM and introduced the knowledge domain of sustainability that is dealt with in this chapter. This knowledge domain is what we label “Knowledge of Sustainability” (KoS). In this section, we extend this discussion by linking sustainability to 2nd generation knowledge management. We aim to show that particularly this combination is essential in order to deal with sustainability and with innovation.
Before going into depth on how 2nd generation knowledge management and sustainability are intertwined, we need to address the issue of artificial systems we mentioned before. Some further introduction into the domain of sustainability is required. Particularly, the history of the concept is of interest here. Many publications in recent history have shaped the sustainability debate as it currently takes place. We highlight two of them. The first publication concerns the report ofthe Club ofRome (Meadows et al., 1972), which addresses the relationship between economic growth and the usage of non-renewable natural resources. Using computer simulations, this think tank predicted a depletion of key natural resources in the first half of the 21st century. Although the publication was strongly criticized, it affected peoples’ mindsets on economic growth around the world. The second publication is one we already mentioned in an earlier section, namely the publication “Our common future” (WCED, 1987). This report places the effects of economic activity in a larger perspective. Besides the depletion of natural resources, pollution and social impacts are presented as issues to be addressed under the umbrella of sustainability. Because of the latter publication, when currently addressing sustainability, social, natural, and economic aspects are considered simultaneously and in an interconnected way. In spite of this broader perspective, nowadays, a strict technological perspective seems to dominate the debate on sustainability. The focus is almost exclusively on environmental and technical issues. Consequently, technical solutions are thought to be the key in resolving issues of sustainability. One seems to focus on increasing the knowledge of a technically oriented sustainability. This is necessary, but only forms half ofthe sustainability issue.
Our perspective on sustainability deviates from a sheer technological orientation, in that we position human behavior (individually and collectively) at the core ofthe sustainability debate. More explicitly, we argue that sustainability is strictly linked to presence (or absence) of human action. This position follows from the distinction we make in natural and artificial systems. A natural system is any system that exists by nature. For instance, a solar system and ecosystem both are considered
natural systems. The artificial system concept originates from a system-theoretical perspective (von Bertalanffy, 1951; Simon, 1969) that is quite common within design and engineering. We define an artificial system as any kind of system that is made and operated by humans. For example, a house, a farm and its farmland are all artificial systems. Whereas the example of the house and the farm are trivial, using farmland as an example of an artificial system is not. Although land is (a) natural (resource) in origin, it also is an artificial system, because the purpose for which it is made and used is human-oriented. For instance, in the Netherlands, all land is artificial. It is conquered from the sea and cultivated. Large parts of the land are demarcated by farmers and arranged to grow a specific kind of crop or let cattle graze. Furthermore, the land is treated in such a way (e. g., fertilization, irrigation) that it provides the highest yield or the most nutritious grass. Using the definition of artificial system and the argument that human choice and action are based on individual and collective knowledge, knowledge is identified as the controlling device of this complex artificial system.
In the previous section, we explained the sustainability of a system as the existence of a balance between the system and its environment. To be more precise, sustainability is an expression ofthe existence of a dynamic equilibrium between an artificial system and its environment (Tietenberg, 2000). Whenever an artificial system uses inputs from its environment, it depends on the capacity of the environment to produce these inputs. Commonly, the sustainability domain recognizes these inputs to be retrieved from sources in the (ecological) environment. The outputs that flow from the artificial system to its environment rely on the capacity of the environment to process these outputs. Outputs are absorbed by systems in the environment, which are commonly referred to as sinks in the domain of sustainability. The precise configuration of the interactions between artificial system and environment, through in - and outputs, results from the behavior of the artificial system. By definition, the behavior ofthe artificial system depends on the behavior of the human(s) and their knowledge operating it, and on the way the artificial system is constructed, very often by humans. Therefore, we see the necessity to innovate and develop knowledge of ecological as well as of social sustainability.
Human behavior and knowledge interact (Jorna, 2006). Knowledge that individuals hold in their minds, shape the world and their view on the world, and consequently the actions they perceive as possible and that enables them to achieve certain goals. Hence, for sustainability depends on human behavior, and behavior depends on knowledge, the knowledge people have determines the sustainability of the artificial systems they control.
From the dynamics of artificial systems and a longing for innovation leading to an increased Knowledge of Sustainability (KoS), we derive another perspective on sustainability, namely Sustainability of Knowledge (SoK). Knowledge of Sustainability (KoS) refers to the content of knowledge; the latter to the knowledge processes that handle KoS. KoS consists of (1) knowledge content about causes that underlie environmental and organizational problems, and (2) the knowledge used to solve such problems. The improvement of organizational and societal behavior, i. e. improving the sustainability of organizations and societies, builds on the problem solving capabilities in which KoS is applied and on the learning processes and its content based upon which KoS is learned. SoK, on the other hand, focuses on the processes that govern the production, creation and integration of knowledge. Sustainable innovation is innovation that centers on KoS and SoK (see Jorna, Hadders, & Faber, 2009; Jorna & Hadders 2010).
Figure 2. Relation between generations of KM, knowledge, learning and sustainability
As we indicate in the section “Knowledge and Knowledge Management”, second generation KM aims to produce new knowledge in addition to 1st generation knowledge management, which aims to stimulate the distribution and
use of existing knowledge. From our discussion on sustainability and the concepts of KoS and SoK, we argue that sustainability is bound to a knowledge management regime that provides the functions of knowledge production, distribution, and use. SoK is realized by 2nd generation KM. Second generation KM ensures the production and integration of knowledge that is pointed out by SoK (Jorna, 2006). In order to contribute to sustainability, KoS should be the content of SoK.
In Figure 2, we combine the various concepts we used in this chapter. We first make a distinction in environment and organization; an adaptive system’s view. The environment also includes the real world, science and stakeholders. From a sustainability perspective the carrying capacity ofthe real world is important (McElroy, 2008). Within (the box of) an organization we discern business or operational processes and operational and strategic management. We also position routine (first order; 1st generation KM) and creative learning (second order; 2nd generation KM). We also include KoS, SoK and Knowledge Management.
Unless we find another planet Earth, the issue of sustainability will stay. As much as we have exhausted our natural and social resources in the last three centuries, just as much will we need existing and new resources to solve our present ecological and social problems. This is an innovative endeavor par excellence. It implies that we have to face and solve poverty, starvation, aging, untimely deaths of newborns and land and water distribution problems as issues of sustainability. Whether this concerns social or ecological aspects is not relevant. In any scenario we need more knowledge of sustainability. This is part of our main message. Another part concerns the focus to shift from only “profit” to “people” and “planet”. We believe that from the perspective of mankind any issue of ecological sustainability is also an issue of social structure and vice versa. This makes sustainability a complex and multi-layered topic. This has already been the case since ages. What has changed, however, over the last three hundred years and culminating the last fifty years, is an ever-increasing exhaustion of our natural resources. What we did in the last (ten) thousands years in situations of shortage is looking for new possibilities and frontiers that can be conquered. In addition, we succeeded in sending out expeditions to new areas and in developing new instruments, technologies and science. That game can partly not be played anymore. We reached the borders of our possibilities and resources. New land, alas, is in outer space and with more than 10 billion human beings in 2050, all requiring fulfillments of their basic needs, we have to do different things, but we especially have to do things differently. We have to discover new tools, instruments and methods. Here, the human species has a great advantage compared to other species: knowledge and innovation (as knowledge creation). We can develop new knowledge and we can (re)use existing knowledge. That is what we called investing in Knowledge of Sustainability. In empirical research we showed examples of organizational projects and case studies in sustainability (Jorna et al., 2004; 2006). However, more empirical research is necessary. Also the complex issue of measuring and quantifying aspects ofthe “people” part is extremely important and not solved, yet.
Innovation of products, services and organizational forms is essential in deepening our knowledge of sustainability. However, because sustainability concerns a dynamic balance between a system and its environment, adaptation and updating is of the utmost importance. This can be done by using Sustainability of Knowledge. It concerns doing things differently, meaning emphasizing processes, dynamics and balancing of “people” in organizations. Sustainability depends on knowledge, but social sustainability also requires joint knowledge actions. This requires also a new perspective on innovation. We should not just innovate to foster continuous growth - the economic “profit” dimension -, but innovate to keep steady states going on or to return to a steady state or balance (Daly & Cobb, 1989). This is an enormous challenge to our innovative powers and our knowledge potential. However, there are signs that we can do it, but it requires as much knowledge of sustainability as we can get.