Knowledge Societies Research Paper

When the term knowledge society was coined, the path of societal development was conceived much more clearly. Fritz Machlup (1962) was the first to estimate the contribution of knowledge work to the economy; Peter Drucker (1969) developed guidelines for mastering the discontinuity brought about by information technology and knowledge work; Daniel Bell (1971, 1973) predicted the stage of postindustrial society, in which theoretical knowledge would be the new ‘axial principle’ of society and strategic resource of progress. A parallel line of reasoning was developed by reform Marxists in the so-called Richta report (1969) and the Japanese ‘Plan for an Information Society’ was presented to its government in 1972 (Masuda 1981). These followed a period of improving the empirical data base (Porat 1977), pondering the validity of concepts (Lyon 1988), and comparing different countries (Edelstein 1978).

Scholarly and public attention became steadily intensified and extended the general themes of the societal centrality of knowledge to a broad variety of fields of investigation: the reconstruction of class structure in knowledge society (Schiller 1981, Schiller and Alexandre 1992), its relation to postmodernism (Lyotard 1984, Poster 1990), globalization and world society (Castells 1997), the spread of expert culture (Stehr and Ericson 1992, Jasanoff 1990), the relevance of intellectual capital and its impact on the reorganization of business (Beninger 1986, Stewart 1997), and the role of science in knowledge society (Gibbons 1994). Usually the term is used generically, denoting a comprehensive societal change, but with respect to the uneven distribution of conditions it may be analytically preferable to split the generic term and speak of various patterns of knowledge societies (Stehr 1994).

1. Defining The Concept Of Knowledge Societies

The reality of knowledge societies might have pleased Plato as putting into practice his ideal of philosophy governing society. It could also be taken as fulfilling the predictions of Condorcet (1743–1794) and Comte (1789–1857) according to which knowledge about the (laws of the) development of society would be put in control of shaping its structure. Or else, the machinery of information and communication technology could be taken to have realized the ideas of integrative and long-range planning developed under the auspices of the OECD in the postwar period (Jantsch 1968). None of these visions has even approximately come true. Neither wisdom, nor generally valid law-like insights, nor integrative and comprehensive scenarios are relevant, but knowledge is expected to be helpful in singling out the risks of decision and action. The permanent modernization of societies leaves all actors in successive states of uncertainty, insecurity, and ambiguity. The more information resources are available for interpreting past experiences, signs of change, and options to adapt, the less likely it is that information itself condenses into reliable knowledge and patterns of orientation. Rather, actors are left alone with the choice between doing anything or nothing— except where there is an agency promising to be specialized in knowing how to read and use information.

Whether, of course, such a promise is reliable, may again be a matter of information which can perhaps be obtained by another agency specialized in watching closely the operations of other knowledge interpreters. The scope of work performed by knowledge agencies covers a broad variety of activities. In many cases knowledge operations become standardized and implemented into machinery and computer-aided systems. To this extent they operate as reliably as other technological tools. Still, there seems to exist a permanent tension between modeling and routinizing processes and structures, and their appropriate use in organizational and individual action. Changing conditions and situational specifities ensure that knowledge tools ‘are not simply applied but processes to be developed’ (Castells 1997, p. 32). The tension between knowledge as application of certified truth and knowledge as risky investigation of what has become and is going to become uncertain can be discussed on three different levels: industrial routinization of knowledge (Sect. 2.1); professional expertise (Sect. 2.2); and learning by designing tests and experiments (Sect 2.3).

2. The Production, Distribution, And Consumption Of Knowledge

2.1 Routinization And Management Of Knowledge Work

Knowledge work will always include certain features of individual creativitiy and imagination. But its integration into the institutional framework of society presupposes its transformation from craftsmanship to industry. Though the term ‘industrialization of knowledge’ may sound anachronistic—at least insofar as knowledge society is conceived to be postindustrial (Bell 1973)—some characteristic changes of knowledge work are similar to the transition from craftsmanship to industrial production. One of the essential nineteenth century breakthroughs of the industrial revolution was the development of a branch specialized in producing machine tools (Rosenberg 1972). The general tendency was to dis-integrate the manufacturing of all parts of a product and to specialize in the production of standardized parts of multiple use and high precision. Similarly, the industrialization of knowledge work proceeds by dis-integrating the craftsman style of manufacturing knowledge—a style that is best represented in the tradition of scientific research groups which start by collecting data from events, arranging them in information packages, and providing interpretation by theoretical models. Contemporary knowledge work transgresses the limits of this tradition. This does not imply a fundamental difference between science and knowledge work but assumes science and its institutions to become part of a new production and distribution system of knowledge. The most important step to be taken in this respect is to understand knowledge as something that can be designed, assembled, shaped, combined, controlled, and adjusted by means of knowledge instruments and that can be certified, licensed, and sold (Fuller 1992). The paradigm of such kinds of knowledge is the algorithm, the software core of all intelligent products and procedures. Algorithms are objectified know-how structures that instruct men, machines, and themselves what to do under certain conditions. In this sense, the history of computer and software industry resembles many traits of the machine tool and other hardware industries. Several authors have rightly stressed the differences between ‘Fordist’ mass production and ‘post-Fordism,’ but they overlook the structural similarity and material continuity between industrial and knowledge economies. Viewing know- ledge as a produceable thing allows one to attach to it the juridical label of being a property. Where the boundary should be drawn between knowledge as either a collective and public or a corporate and proprietary good is highly controversial (Weil and Snapper 1989). Traditional philosophical understandings of knowledge, as well as the received view of professional work as applying disciplinary academic knowledge to single problems, are no longer applicable to most variants of knowledge work. The concept now refers to activities that can be characterized by importing, improving, combining, assessing, and testing knowledge by means of informatized machines, instruments, toolsets, networks, and media in order to produce expertise (Reich 1991). Having stressed the industrial aspects of knowledge work as they become manifest in material and immaterial knowledge machinery, it is only fair to point out the differences. The dynamics of knowledge work tend to create individualistic cognitive styles with respect to methods, problem selection and definition, and communication with partners and clients. It is hence by no means certain that different approaches will produce identical results. This, in turn, provokes a fundamental conflict for the management of knowledge work. On the one hand management must try to increase complexity by bringing together different approaches to defining problems, framing options, and designing solutions. On the other hand it is important to quickly reduce complexity by generating trust in prototypes, which can be tested and improved with selected users (Leonard-Barton 1995). If the main task of knowledge management is the organization of innovation, it does not leave untouched organizational design (Myers 1996). Bureaucracy needs to be replaced by knowledge links, flexible networks, and strategic alliances. Organizations not only have to support the idiosyncratic aspects of knowledge work, but also to turn into learning institutions, which work with visions, experiments, and assessments in order to permanently define the environment to which they want to adapt (Argyris and Schon 1996).

2.2 Facing The Unknown

There is a complementary aspect to the economic and/organizational characteristics of knowledge society. Certainly, knowledge would not have become a predominant resource if it could not become transformed in marketable goods and services. It is not less true, however, that every state of knowledge opens up even more notions of what is not known. The more society relies on knowledge, the more it navigates into a dynamics of change. To understand, describe, predict, construct, and control this change becomes illusory—simply because of lack of knowledge.

This is exactly the reason why knowledge experts (Jasanoff 1990) and symbolic analysts (Reich 1991) are needed and expected to create islands of certainty to allow orientation and planning. Demand for knowledge expertise may differ in several respects: by its purposes, ranging from understanding to predicting and constructing; its form of communication, which may be more propositional or more practically embedded; and its scope of application. It may be a rather general theoretical belief system, a generalized rule, a simulation model, a special case experience, or just the solution of an urgent problem. Wherever such expertise is invested, the expectation is to obtain some certainty. But certainty does not exist only in degrees of quality ranging from absolutely valid, to reliable under certain conditions, to better than nothing. It is also exposed to a second-order knowledge about knowledge, or expertise on expertise. Every form of expertise depends on choices with respect to heuristics, methods, or data bases and could have been formulated differently. Where there are choices there are risks. But if, in turn, risks can only be reduced by additional expertise, then the net result may be a steady loss of certainty in society. Even if infinite amounts of information were accessible and could be processed by advanced knowledge technologies, the well-known problem of acting under bounded rationality would not be solved but enhanced. The constant examination and reformulation of social practices in the light of incoming information about those very practices is also conceived in Giddens’ model of ‘reflexive modernization’ (Giddens 1990).

2.3 Knowledge And Science

It is important to relate the characteristics of knowledge society to those of science in society. While on the one hand large-scale expertise can be interpreted as spreading the methodical and theoretical potential of science to highly specialized problems and as a consequence of a job-seeking academically trained labor force (Stehr 1992), the differences between expertise and research are obvious. Research is performed in an institutionally (laboratories) and cognitively (theories) separated world, in which failures and errors are highly welcome. Everything can hypothetically and experimentally be pushed to the extreme and only in a few cases are legal and social norms violated. Society is only affected indirectly by research, namely by transforming research results into technologies or ideologies. Even if this view meets a convenient self-description of the scientific community rather than historical facts, expertise could rarely be sold as a piece of hypothetical reasoning to which those who apply it provide the testing field. Still, it is instructive to underpin the intersections of expertise and research.

While many branches of science tend to derive expert knowledge directly by extrapolating from isolated theoretical worlds to the real world (of health, education, international affairs, social structure, development, etc.), expertise tends to not only apply certified knowledge but to do research. Consequently, knowledge society can be interpreted as being exposed to hypothetical assumptions and experiments designed by experts and analysts. While expertise effectively reduces the complexity of the real world for an observer, it inevitably contributes to increase the risks of those who decide to rely on it.

3. Critical Aspects Of Knowledge Society

This ambivalent status of expert knowledge between disinterested scientific neutrality and action-oriented advice has given rise to criticism of expert culture and its ‘politics of knowledge’ (Cozzens and Woodhouse 1994). Another problem to be addressed in the future is the deficient quality management of expert knowledge and the inadaequacy of licensing and accreditation mechanisms. A profound cognitive aspect of criticism relates to the loss of personal experience and judgement and its substitution by mediated images, simulations, screens, and spectacles (Morley and Robins 1995) along with a new potential for ideological manipulation (Schiller 1981). The intraas well as international boundaries between those who have or do not have technical and intellectual access to information (information rich vs. information poor) have given rise to neo-Marxian class analysis of knowledge societies (Schiller 1992). These and other controversial aspects (see Webster 1995) indicate the deep structure and far-reaching consequences of the ongoing transformation toward knowledge society.

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