Innovation through knowledge codification

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Journal of Information Technology (2001) 16, 83–97

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CA RSTE N SØR ENSE N Department of Information Systems, The London School of Economics and Political Science, London, UK

U LRIKA LUNDH -SNIS Laboratorium for Interaction Technology, Trollhättan Uddevalla University, Uddevalla, Sweden

Academics and business professionals are currently showing a signiŽ cant interest in understanding the management of knowledge and the roles to be played therein by information and communication technology (ICT). This paper takes a closer look at one of the primary issues raised when supporting the management of knowledge: how to understand the role of knowledge classiŽ cation and codiŽ cation as means for further organizational learning and innovation. Two manufacturing cases are analysed using particular perspectives from current theories on classiŽ cation, namely the management of knowledge and organizational innovation. It is concluded that a more complex understanding of the interplay between cognitive and community models for knowledge management as informed by research on the social processes of classiŽ cation can inform our understanding of both the role of classiŽ cation of knowledge for organizational innovation and the viability of providing ICT support based on codiŽ ed knowledge.

Introduction To date, current knowledge management debate has shown many interesting facets. It represents a multitude of perspectives each with its own distinct agenda and assumptions. As a research Ž eld it is grounded in a broad and loosely deŽ ned agenda allowing researchers and practitioners with differing perspectives to engage in a rich but also often somewhat confusing debate given that there are several perspectives as to what might be considered to be knowledge and the reasoning about the role of knowledge in organizations (Kakihara and Sørensen, 2001). The debate has been concerned with issues such as (1) knowledge work as a re ection of structural changes in society (Drucker, 1993), (2) knowledge as the most important resource in the Ž rm (Grant, 1996; Prusak, 1997), (3) the distributed nature of organizational knowledge (Tsoukas, 1996), (4) the creation of knowledge involving translation back and forth between tacit and explicit (Nonaka, 1994; Nonaka and Takeuchi, 1995) and (5) the individual and social aspects of knowledge creation (Spender, 1996, 1998). Generally, critical assumptions regarding the nature of organizational knowledge are made, for example in terms of the entitative perspective on ‘knowledge’ as opposed to the performative action-orientated perspective of ‘knowing’ (Blackler, 1995; Boland and Tenkasi, 1995). Integrative or pluralist frameworks relating the management of knowledge to learning have been

suggested to accommodate these competing assumptions, for example those of Spender (1998), Cook and Brown (1999) and Ciborra and Andreau (2000). However, the viability of categorizing types of knowledge, such as the tacit/explicit distinction, has been questioned by some (Blackler, 1995) and forwarded as a necessity by others (Cook and Brown, 1999). Thus far the debate has been both principled and pragmatic at the same time. The exploration of information and communication technology (ICT) as a means of supporting the management of knowledge has in particular proven an interesting and potentially contentious research topic (Boland and Tenkasi, 1995; Alavi and Leidner, 1999; Scarbrough et al., 1999; Swan et al., 1999a; Robertson et al., 2000). Research informed by social science theories argues for an increased focus on human and social issues, such as organizational learning and the management of human resources. At the same time, there is signiŽ cant interest within computing, engineering and industrial communities in developing and using increasingly complex knowledge management systems (Swan et al., 1999a,b). Comparing the technological scepticism within the social science community with the technological optimism within the engineering and business community, there seems to be a need for a more principled debate of the possibilities for and limitations of ICT in supporting management of knowledge in organizations. Such a debate should be informed by organizational theories, theories on the use of ICT and speciŽ c

Journal of Information Technology ISSN 0268–3962 print/ISSN 1466–4437 online © 2000 The Association for Information Technology Trust http://www.tandf.co.uk/journals DOI: 10.1080/026839600110054771

Sørensen and Snis

84 knowledge about the contemporary technological options that are available. This paper further suggests that investigating how to support the generic management of knowledge is somewhat futile. It cannot be assumed that knowledge is managed in a similar fashion in all organizational settings and across sectors and company sizes. Importantly, neither can ICT be perceived as one technological ‘black box’. Furthermore, in supporting the management of knowledge, the authors believe it to be highly problematic, a priori, to assume certain allocations of functionality between humans and technology as well as between manual and computer-based information technologies. Successful use of ICT necessarily relies on a complex pattern of manual and technologically driven activities. Some aspects of an ICT system will be manual, e.g. forms, classiŽ cation schemes, boards, notes, written procedures, etc. and, inherently, other aspects will be computer based. Inspired by an earlier debate within the Ž eld of computer-supported collaborative work (CSCW) on classiŽ cation and ICT support (Suchman, 1994; Winograd, 1994), this paper looks at the role of the classiŽ cation or codiŽ cation of knowledge in organizational innovation. This is particularly signiŽ cant for organizational innovation and ICT support for the management of knowledge. The role of socially constructing classiŽ cations cuts to the core of both managing knowledge and providing ICT support in that technological support for the management of knowledge will inevitably rely on some form of classiŽ cation. The potential contentious nature of forming categories (Suchman, 1994) and the need for categories as means of managing organizational complexity (Winograd, 1994; Carstensen and Sørensen, 1996; Schmidt and Simone, 1996) presents a challenge to our understanding of how to provide ICT support for this activity. Furthermore, there is a lack of research focusing on the detailed processes of technical innovation and learning through codiŽ cation. As argued by Bowker and Star (1999), ‘Traditionally much ethnoor folk-classiŽ cation research has examined tribal categories in nonindustrial societies. How people in industrial societies categorise on an everyday basis is less well known, especially in natural workaday settings’ (p. 59). The two case studies presented here speciŽ cally focus on manufacturing domains as examples of organizational innovation. In both cases the codiŽ cation of manufacturing knowledge played a central role. The original Ž eldwork was conducted from the dual perspectives of expert systems and CSCW respectively. However, these cases highlight the relationship between the creation of knowledge and the process of embedding that knowledge in systems that are subsequently deployed in the organization. The aim of the reanalysis of these two cases was to bring some of the concerns from these Ž elds into the debate regarding ICT support for the manage-

ment of knowledge. This paper provides a rich and contextual understanding of the relationships between the processes of codiŽ cation of manufacturing knowledge and the potentials for ICT support. The analysis is based on theories of the interrelationships between classiŽ cation systems and the social processes of constructing and using classiŽ cation systems (Bowker and Star, 1999), which also relates to theories of knowledge and knowing (Blackler, 1995; Spender, 1996, 1998; Cook and Brown, 1999). In order to situate the discourse of codiŽ cation of knowledge within the context of organizational innovation, the paper draws upon models linking the management of knowledge with organizational innovation (Brown and Duguid, 1991; Swan et al., 1999a; Swan and Newell, 2000). The paper highlights the importance of distinguishing between the innovation process and product as well as between types of ICT products when analysing models of knowledge management applied for the classiŽ cation of knowledge. The next section highlights the main theoretical perspectives used in this paper for analysing classiŽ cation, the management of knowledge and organizational innovation. The third section discusses the methodology and the fourth section presents and analyses the two cases. The Ž fth section discusses the Ž ndings and relates them to contemporary knowledge management literature regarding codiŽ cation, organizational innovation and ICT support. The Ž nal section concludes the paper.

ClassiŽ cation, knowledge management and innovation This section brie y outlines relevant theories concerning the social aspects of classiŽ cation, knowledge management and the relationships between knowledge management and organizational innovation. ClassiŽ cation This paper aims to analyse the role of classiŽ cation and classiŽ cation systems for process innovation in manufacturing. Therefore, an understanding of both social and technological aspects of classiŽ cation and codiŽ cation is at the centre of the analysis. ClassiŽ cation is deŽ ned as spatial, temporal or spatiotemporal segmentation of the world exhibiting the following abstract characteristics: consistent and unique classiŽ cation principles in operation, mutually exclusive categories and a complete system classifying everything under consideration (Bowker and Star, 1999). If the classiŽ cation structure has emerged as the result of multiple and possibly con icting classiŽ cation principles, it is called a

Innovation through knowledge codiŽ cation nomenclature. CodiŽ cation is related to classiŽ cation in that it means reducing to code and is synonymous with arranging, cataloguing, classifying, condensing and organizing (Infopedia, 1996; Roget’s Thesaurus and Webster’s Dictionary). According to Bowker and Star (1999) classiŽ cation is often associated with ubiquity in the sense that classiŽ cation structures and classiŽ cation work often goes on unnoticed within everyday working life. ClassiŽ cation activities depend on the use of a multitude of standards and classiŽ cation structures. ClassiŽ cation schemes are both material and symbolic, often implementing a mixture of physical entities, e.g. paper forms and software instructions and conventional arrangements such as speed and dimension. ClassiŽ cations suffer from the indeterminacy of the past in that our knowledge of the past is constantly revised in the light of new developments. In addition, classiŽ cations are constructed and reproduced through detailed negotiations, organizational processes and con ict according to the practical politics of classifying and standardizing. Finally, the dialectics between formal classiŽ cation and the practice of interpreting the formal structures is one of the key issues in providing ICT support for collaborative work activities (Winograd, 1994; Bowker and Star, 1999; Schmidt, 1999). This also implies that studies of classiŽ cation have tended to move away from dichotomizing the formal and the informal aspects of classiŽ cation (Bowker and Star, 1999, pp. 54 and 56). Knowledge creation and knowledge management For the purpose of the analysis presented here it is assumed that organizational knowledge is highly distributed (Tsoukas, 1996). Therefore, when studying the classiŽ cation and codiŽ cation of technical knowledge several perspectives can be of importance, such as the role of the implicit and the tacit in knowledge creation (Nonaka, 1994; Nonaka and Takeuchi, 1995), the individual and social aspects of knowledge creation (Spender, 1996, 1998) and the role of geographical and conceptual situatedness (Nonaka and Konno, 1998). All the major frameworks, including those of Cook and Brown (1999), Spender (1996, 1998) and Nonaka (1995), distinguish types of knowledge, for example tacit/explicit and individual/collective. Cook and Brown (1999) aimed to integrate the concern for the individual possessive perspective with a collective in terms of communities of practice (Brown and Duguid, 1991) and an action-orientated perspective, for example as expressed by Blackler (1995). They argued that practice is distinct from both action and behaviour and deŽ ned practice as ‘action informed by meaning drawn from a particular group context’ (Cook and Brown, 1999, p. 387). Practice is what converts tacit knowledge into explicit

85 knowledge. This, they argued, will integrate knowledge and knowing and the two interactively constitute each other. Our interest here is in investigating the practice of both classiŽ cation and codiŽ cation of knowledge as an organizational innovation process, as well as the system emerging as its product. Knowledge management and organizational innovation Swan et al. (1999a) formulated two distinct perspectives on knowledge management for innovation, namely the cognitive and community models. The community model is formulated as a critique of the predominant cognitive perspective within the technology-driven research Ž eld (Scarbrough et al., 1999; Swan et al., 2000). The cognitive model denotes a perspective where valuable knowledge is conceived as being captured and codiŽ ed from individuals, packaged, transmitted and processed through the use of ICT and, hence, disseminated and used by other individuals in new contexts. In this perspective, knowledge can also be exploited through the recycling of existing knowledge that is ‘owned’ and ‘experienced’ by individuals in cognitive networks. Here, ICTs are seen as critical success factors. In contrast, the community model portrays the management of knowledge as socially constructed through interaction within communities of practice. Communities of practice consist of collections of individuals between whom there is collaboration and negotiation. Knowledge creation and learning are processes making sense of knowledge in social activities that are deeply rooted in daily work practices. Within the community model, ICT can play a role, even though it is not seen as a critical success factor. Table 1 summarizes the main characteristics of the cognitive and community models. On reviewing the literature, it was found that the distinction between a cognitive model on the one hand and a community model on the other appropriately describes the two main approaches both to knowledge management, but in particular to a debate of the role of ICT in the management of knowledge. The cognitive model, which focuses on the crucial role of technology as the mediator of codiŽ ed knowledge, represents the technologist view and promotes the view that knowledge can be managed by codiŽ cation. The community model, which focuses on social interaction and negotiation, promotes the idea of supporting interaction and collaboration in order to manage knowledge. When considering issues related to the creation and sharing of technical knowledge and to the role of technical and organizational measures, in doing so we are left with the question of who to trust: the technologists touting the splendour of technology or the social scientists questioning this view? This is particularly interest-

Sørensen and Snis

86 Table 1

Two contrasting views of the knowledge management process (from Swan et al., 1999a)

Cognitive model

Community model

Knowledge for innovation is equal to objectively deŽ ned concepts and facts

Knowledge for innovation is socially constructed and based on experience

Knowledge can be codiŽ ed and transferred through text: information systems have a crucial role

Knowledge can be tacit and is transferred through participation in social networks including occupational groups and teams

Gains from knowledge management include exploitation through existing knowledge

Gains from knowledge management include the recycling of exploration through the sharing and synthesis of knowledge among different social groups and communities

The primary function of knowledge management is to codify and capture knowledge

The primary function of knowledge management is to encourage knowledge sharing through networking

The critical success factor is technology

The critical success factor is trust and collaboration

The dominant metaphors are the human memory and the jigsaw (Ž tting pieces of knowledge together to produce a bigger picture in predictable ways)

The dominant metaphors are the human community and the kaleidoscope (creative interactions producing new knowledge in sometimes unpredictable ways)

ing when considering the need for codifying knowledge into classiŽ cation structures as means of organizational learning, innovation and support for the management of complexity (Winograd, 1994; Schmidt, 1999) In the following this paper will critically seek to reappraise the relative roles of the community and cognitive knowledge management models in understanding organizational innovation in manufacturing through the codiŽ cation of manufacturing design knowledge.

Reanalysing two cases from manufacturing The cases are both drawn from studies where qualitative interviewing, participant observation and document inspection were the three primary data collection methods used (Patton, 1980; McCracken, 1988; Cash and Lawrence, 1989; Carstensen and Sørensen, 1994). Both studies, but in particular case A, contain elements of action-based research (Braa and Vidgen, 1999). The data were not intentionally collected for this particular analysis. However, the focus of both studies was to consider the relationships between everyday manufacturing processes and the development and use of ICT. The intention is not to communicate the full contextual characteristics of the case studies. Instead, the purpose is to focus on a few aspects that are pertinent to the discussion of organizational innovation processes, the codiŽ cation of knowledge and issues concerning ICT support. Both cases focus on process innovation – case A within a particular, complex manufacturing process (Snis, 1997) and case B in the engineering design and documentation speciŽ cations department (Sørensen,

1994; Carstensen and Sørensen, 1996). In the following the paper brie y outlines the original methodologies applied for data collection and the following two sections then present and discuss the cases in detail. Case A: Volvo Aero, Sweden Case A represents a particular aspect of the Ž eldwork conducted in the thermal spraying department at Volvo Aero Corporation in Trollhättan, Sweden. The company develop, produce and maintain jet engines for military as well as civil use (Snis, 1997). Initially, there were vague understandings about the problem domain. Thus, an explorative investigation was initiated before outlining the issues of concern. The empirical material was collected over a period of 16 months from semistructured interviews with 19 employees – three material engineers, two laboratory engineers, two managers, Ž ve production engineers and seven operators. The study also involved participation in a number of meetings concerning the thermal spraying process, participant observation with the people interviewed and analysis of internal documentation from the thermal spraying department. A generic interview protocol was used in order to identify relevant questions and problems for further exploration. As the study progressed, common themes began to emerge and a reference group of major stakeholders in the problem domain was formed in order to discuss the relevance of the empirical Ž ndings. The purpose of the study was to establish the requirements of an expert system in supporting the improvement of the quality of the thermal spraying process.

Innovation through knowledge codiŽ cation Case B: Foss Electric, Denmark Foss Electric is a Danish manufacturing organization that develops, manufactures and markets instruments for automatically measuring the quality parameters of agricultural products, such as measuring the compositional quality of milk, the composition and microbiological quality of food products and the quality of grain. Designing these instruments involves a range of expertise, for example from the disciplines of mechanical, chemical, electrical and software engineering. The organization has implemented concurrent engineering methods in a matrix organization in order to shorten its lead-time. The aim of the empirical effort at Foss Electric was to analyse cooperative work in a manufacturing setting where the participants deal with the complexity and uncertainty of going from a design idea to determining how to manufacture the product. Two computer scientists and one manufacturing engineer studied the engineering design, process planning and software design at Foss Electric. Approximately 20 open-ended interviews were conducted (Patton, 1980). More than ten project meetings were observed and just over 100 person hours of project observation were conducted over a period of approximately 4 months. This project observation was followed up by several meetings with the project members. Observations and interpretations of their work were presented at these meetings and feedback was

87 obtained. The research approach used in collecting data at Foss Electric can be characterized as qualitative research inspired by ethnographic approaches to studying collaborative work (Heath et al., 2000) and engineering work (Bucciarelli, 1984).

Two cases of knowledge codiŽ cation Case A: codifying thermal spraying in order to improve process quality The manufacturing process of thermal spraying plays an important role in ensuring the highest attainable quality in new jet engine designs. Consequently, over the years the organization had developed considerable high-tech competence in this manufacturing process and 40 people were employed in the thermal spraying department, including two managers, three material engineers, two laboratory engineers, Ž ve production engineers and seven operators who were responsible for the masking, cleaning and spraying components. Thermal spraying involves partly melting and throwing a material, usually a powder, onto a substrate where a coating is built up by the condensing particles (see Figure 1). The application and the desired properties of the coating determine possible spraying methods and materials. Metals, alloys, carbides, plastics and composites can be applied by thermal spraying.

Figure 1 A schematic drawing of the thermal spraying process indicating the four main categories of variables affecting the process and product quality

88 Thermal spraying has had a troubled history concerning quality assurance. The multitude of factors affecting the quality and the difŽ culties involved in measuring, estimating and testing process quality result in problems of consistency and reproducibility. The quality of the coating is in uenced by a complex set of parameters. For example, in plasma spraying approximately 50 macroscopic parameters need to be adjusted. The set point determination of the process parameters is often a matter of trial and error and is time-consuming. Moreover, the stress build-up in the coating, which is determined by the cooling conditions of the droplets on the surface and of the successive deposited layers, vary as the coating grows due to changes in the local thermal Ž eld. In state-of-the-art thermal spraying, the set point parameters are determined for the entire duration of the spraying process with no consideration to the changing conditions at the coating surface during spraying. These factors lead to coatings with variable quality due to lack of control of defects such as microscopic cracks and porosity. This lack of quality and reproducibility increases the manufacturing costs and limits the expansion of areas for applying thermal spraying. At the time of study, rapid changes were being introduced across the world, applying advanced continuous measurement and data processing systems with the purpose of ensuring a higher quality thermal spraying process. The types of problems which thermal spraying employees face can be summarized as incomplete information about the state of the process and inconsistency and uncertainty of what knowledge to collect, codify and apply to the process. In order to improve the quality of its thermal spraying process, the organization under study had initiated a research project with the purpose of investigating the main factors affecting process quality. Part of this project involved critical appraisal of the management and coordination of primarily tacit knowledge with the purpose of codifying this knowledge into an expert system (Snis, 1997). It was intended that the expert system would intentionally allow dissemination and distributed application of the codiŽ ed process knowledge through a technology-driven interaction between a human operator (allegedly) without deep domain knowledge and a computer-based expert system on thermal spraying processes (Russel and Norwig, 1995; Turban, 1995). The project did not involve the task of automating existing explicit rules and previously codiŽ ed principles. Rather, the task of the knowledge engineer was to elicit complex and tacit process knowledge situated throughout the various professional groups involved carefully, thus making it explicitly formulated as rules that could be codiŽ ed into a new expert system. As a result of preliminary interviews, more questions were raised than answered and the extremely complex nature of the tacit

Sørensen and Snis process knowledge was identiŽ ed. One such example was the crucial local adjustments requiring both experience and skills made by the operators who monitored and controlled the process. These were made as a direct result of observed process anomalies, such as a ‘strangely’ shaped  ame that was caused by the power feed in the robot gun being too low. The study demonstrated that it was not only technical factors that affected the quality of the process. It highlighted the crucial role of human judgement, as exempliŽ ed in the experience of the operators, personnel training and education, collaborative efforts among work group members, etc. Internal inconsistency within the rule base for quality highlighted another example of the problems of codifying the tacit process knowledge. Pre-established different and con icting rules meant that this inconsistency was premised on different subactivities and roles. As a direct result of these signiŽ cant problems, the expert system project was ultimately abandoned. This case exempliŽ es an organization looking for a technical Ž x. The aim was to resolve a very complex issue of manufacturing process improvement through a fairly straightforward although mentally complex individual ‘knowledge elicitation’ process. The process of codifying the complex, distributed and partly con icting process knowledge was sti ed because the knowledge elicitation process was conducted by one knowledge engineer who was not an expert on thermal spraying but a specialist in expert systems. A hypothetical collaborative negotiation process would have had to take into consideration the different perspectives of the people involved in the various aspects of thermal spraying, such as robot operators, robot programmers, engineering designers, process planners, quality assurance experts and project managers. The people in thermal spraying did not work as a community and they did not share a common view. The fact that the knowledge engineer interviewed people individually and discussed the possible codiŽ cation of the rules governing the thermal spraying process with them did not amount to a proper unfolding of collective knowing – the participants did not engage in a generative dance involving individual, collective, tacit and explicit knowledge on the one hand with the deeply rooted practice of thermal spraying operational work on the other (Cook and Brown, 1999). Constructing an expert system would have been an effort associated with great risk, given the high degree of complexity and the unfavourable environment (Mockler, 1992). There were far too high expectations surrounding a technological solution. The knowledge that was to be codiŽ ed was deeply rooted in human expertise, an expertise that would require signiŽ cant collaboration and negotiation to even begin to formulate it. Had the Ž rm somehow succeeded in the endeavour, the solution would have constituted a fairly

Innovation through knowledge codiŽ cation complex knowledge management technology in combination with a relatively simple organizational solution in terms of individual members of the process being able to consult the expert system. As far as the participants were concerned, the problem was purely a technical one. This implied that the solution should be found in the technical domain. Attempts to persuade them that thermal spraying is a sociotechnical process proved quite difŽ cult. The attention of the organization was closely focused on the technical parameters and a closer inspection of the espoused theories on the factors affecting the quality of the manufacturing process demonstrated a rich, socially constructed picture of both con icting and non-technical explanations. This case thus exempliŽ es codifying and ‘packaging’ individual, conscious knowledge with the purpose of transforming it into objectiŽ ed knowledge via an expert system using an objectiŽ cation strategy for knowledge communication (Scarbrough, 1995; Spender, 1996). Case B: collaborating in order to codify engineering design models At the time of study, Foss Electric had just adopted computer-aided design (CAD) workstations as a replacement for conventional paper-based design tools. This could facilitate faster development processes, less uncertainty regarding subassemblies and a more rapid transfer from engineering design via process planning into production. The introduction of CAD workstations also created the need for reuse of speciŽ cations. One of the advantages of using CAD instead of a traditional paper-based system was the improved opportunity for reusing old components in new products or at least reusing the speciŽ cations of standard components. This could both save time and support standardization. However, the standardization of components and reuse of component speciŽ cations across projects was crucially dependent upon distributed storage and retrieval of CAD speciŽ cations across temporal and spatial barriers. Categorizing a CAD speciŽ cation would enable one person to store it and another subsequently to Ž nd and reuse it. Hence, in relation to introducing CAD, the company developed the classiŽ cation scheme illustrated in Figure 2, which captures the components and units for all instruments produced. It was ordered in a tree structure with classes, categories, subcategories and sub-subcategories. There were 16 classes and approximately 357 different subcategories, categories that were one to four levels down from the class. As an example, class number 5 was hydraulic and pneumatic components, which had 11 categories. One of these was valves, which had six subcategories and no sub-subcategories (see Figure 2). At

89 the most detailed level, each sub- or sub-subcategory was speciŽ ed in a data management system according to predeŽ ned database records. This speciŽ ed the type of data saved about a speciŽ cation in greater detail, such as physical characteristics, the name, what project it was speciŽ ed in, who stored the speciŽ cation, the inventory part number and the material and form. The product classiŽ cation scheme provided a conceptual structure, thereby making it possible for draughtspersons and engineering designers to perform distributed storing and retrieval of CAD models. It re ected a common standard for categorization of the components and units in any instrument produced by the company and can be viewed as the negotiated order of how an instrument could formally be described. The classiŽ cation scheme was partly paperbased represented on A3 size prints and partly computer supported by an alphabetically ordered list of categories integrated into the CAD system. The classiŽ cation process was not stipulated in any explicit organizational procedure. The draughtsperson performing the classiŽ cation chose the appropriate category from the printed scheme and subsequently entered it into the data management system by selecting the appropriate category from the list of categories in the CAD system The classiŽ cation scheme appears to be more a nomenclature than a ‘proper’ classiŽ cation structure in that it is not constructed from one governing principle. Instead it can be viewed as the pragmatic result of negotiation and the need for a working model of the products designed at Foss Electric. The structure displays several ‘other’ categories at several levels enabling the everyday accomplishment of classiŽ cation work to run smoothly. Category 13 is a whole class that is intended to capture any mechanical component not captured in any other place in the structure and there are ‘other’ categories scattered throughout the scheme at a lower level. In this respect the scheme is a good example of the day-to-day balancing of the formal and informal aspects of classifying. The classiŽ cation system is thus prototypical and is based on an emerging shared understanding of how to classify components. The structure represents a boundary object in that it spans several professions within Foss Electric, from the draughtsperson conducting the initial classiŽ cation, through the engineering designer reusing the speciŽ cation in a subsequent design, to the process planner who uses the detailed speciŽ cation. The textuality of the classiŽ cation scheme is manifested in the physical layout and numbering of the general categories. Interestingly, the sequencing of categories is made according to the inner logic of balancing the importance of class with the sequencing of activities, hence category 1 electrical and category 2 electronics before category 5 hydraulics and category 11 casing. The sequencing of categories pro-

10. Fixing components Bolt Machine bolt Self grinding Six-edge bolt Internal six-edge Nut Normal contra PE nut Lock nut Disc Spring disc Plane disc Star disc (Six more)

1. Electrical connection component Plug Simple Complex Coax Flat cable plug Electrical connector Solder corner Solder socket Cable shoe Cable Cable bunch Cable with plug Net cable Multicable Wire with plug

3. Measuring component/ Signal source Measurement component Signaller

11. Casing Plate casing Cabinet accessories

5. Hydraulics/Pneumatics Fitting Membrane Valve Reduction valve Safety valve Tube valve 2/2 valve 3/2 valve Magnet system Manifold Tube Filter Pump Pressure cylinder Piston cylinder (Two more)

7. Spring/Damper Spring Damper

8. Transmission component Transmission mechanical Transmission optical Transmission thermal

6. Seals/Protection Seal component Protection component Shielding

4. Optics 16. Paperwork Mirror Prism Lens Beam splitter 15. Signs (Seven more) Warning Other Type Name Instruction

Other component Other units

13. Other mechanical components/units

12. Media handling Transfer Measure Mix React (Ten more)

Figure 2 The product classiŽ cation scheme, as printed on an A3 sheet of paper, outlining the 16 main groups, some of the 79 categories, examples of the 177 subcategories and none of the 99 sub-subcategories or two sub-sub-subcategories

2. Electronics Electromechanical Motor Ventilator Electromagnet (Eleven more) Electrical Resistance Condenser Diode (Three more) Electrical junction Mains Display (Three more)

14. Instruments Complete instrument Part specification Specialist tool

9. Distancing component Rod (Three sub Êcategories) Tube Ring Track

90 Sørensen and Snis

Innovation through knowledge codiŽ cation duces an uneven textuality when printed on an A3 sheet of paper but makes perfect sense when each category is picked from a sequential list within the CAD system. SigniŽ cantly, the initial scheme was designed by an ad hoc group of six people who spent a number of weeks designing it in their spare time outside of normal working hours. The work of determining the categories promoted major discussions and even Ž erce arguments amongst the members of the group. As in the case of the International ClassiŽ cation of Diseases (Bowker and Star, 1999), the use and management of the product classiŽ cation scheme can be characterized as a struggle for carefully Ž nding ‘the appropriate level of ambiguity’. It was not a primary requirement that any CAD speciŽ cation stored by one person should be easily retrievable by another; rather, the idea was to support retrieval of the most commonly used components and subassemblies. These components were most often characterized by a very well-deŽ ned functionality and, therefore, the classiŽ cation scheme was primarily based on functionality. New components and units were constantly designed as a result of technological innovation in measurement technologies and manufacturing processes, at times making it difŽ cult to perform the classiŽ cation. However, this did not result in constant changes to the classiŽ cation scheme. Because of the highly distributed use of the scheme, changes had to be negotiated. The categories were modiŽ ed from time to time and new categories were added in order for the scheme to represent the type and function of the components speciŽ ed. Changes to the scheme were the results of negotiations between designers and draughtspersons at designated meetings. The classes and categories in the scheme were based on a mix of the functional and geometrical properties of the components and units. Some of the categories re ected the practical problems of classifying components. For example, class 13, which was named ‘other mechanical components and units’, contains categories such as ‘console’, ‘plate’, ‘cylindrical component’ and ‘tube component’. This gave the draughtsperson a means of classifying non-standard irregular components, which otherwise would have been impossible to Ž t into the scheme. However, it was reported by several interviewees that the people who had originally designed the scheme found it much easier to use than people who had not been directly involved. This case demonstrates some radically different characteristics to those of case A. The organizational problem was relatively simple, i.e. coordination support for distributed storage and retrieval of CAD speciŽ cations with the purpose of reuse. However, the problem was addressed by a relatively complex process of interaction between a group of six domain experts with deep knowledge. These individuals spent their spare

91 time over several weeks arguing and negotiating until, in functional terms, they were able to present a theory of what the company was all about. Amongst the subgroup this represented a legitimate example of collective classiŽ cation work where deep prior experience from practice was allowed to engage in interaction with espoused and possessed theories of the domain of work considered (Bowker and Star, 1991; Cook and Brown, 1999). The solution was, in technological terms, stunningly simple. It consisted of a tree structure to be used in association with implicit conventions for classifying, rendering the use of the technology reliant on tacit knowledge. Knowledge was communicated through a combination of objectiŽ cation (i.e. the classiŽ cation structure) and professionalism (via the tacit conventions that were unconsciously applied when using the scheme) (Scarbrough, 1995). However, the process that brought about the classiŽ cation scheme was mainly driven by professionalism in the sense that constant negotiation and discussion were the primary means of bringing about or creating the ‘packaged’ knowledge. In Spender’s (1996) terms, the individuals in the ad hoc group combined their conscious and automatic knowledge, thereby resulting in the development of the collective knowledge necessary for producing objectiŽ ed knowledge residing in the classiŽ cation structure. This case is a good example of the community model of knowledge management in the sense that the important aspects of knowledge creation or innovation were the establishment of a community of workers negotiating the categories and the structure of the classiŽ cation scheme. Information technology was of course a crucial element, but only in terms of recording the results of the group interaction and as an externally visible representation of the codiŽ cation of tacit knowledge. Summary The two cases discussed above can be accommodated largely within the classiŽ cation scheme developed by Swan et al. (1999a), which highlights the cognitive and community approaches to the management of knowledge. Case A displayed a particular conŽ guration of knowledge management models through the process. There was little reliance on the community model since the specialist in expert systems drove the interview and observation process with some debate in a steering committee of stakeholders. The innovation process attempted to go directly from agenda formation to the codiŽ cation of manufacturing process knowledge. From the beginning the project stipulated the selection of expert systems technology with the purpose of codifying organizational knowledge. Case A renders itself as being relatively easy to classify as an example of the cognitive

92 model (Swan et al., 1999a), as can be seen in the lefthand column in Table 2. In case B the organizational innovation process was based entirely on internal knowledge with no need for boundary spanning activities. The main aspect of this case was a complex process of negotiating the design of a large classiŽ cation structure representing a mixture of tacit, explicit, individual and collective knowledge (Cook and Brown, 1999). The right-hand column in Table 2 illustrates case B according to the community model. The authors conclude that the successful codiŽ cation of technical knowledge within manufacturing for organizational innovation must be based on the collaboratively established consensus re ected in the community model. The interdependent actors bringing different skills to highly complex technical work processes will therefore have different perspectives (Schmidt and Simone, 1996). ClassiŽ cation as a social process is therefore subject to continuous renegotiation (Bowker and Star, 1999).

Discussion When analysing the two cases, more substantial insights can be gained into the complex interrelationships between models for knowledge management, classiŽ cation work, organizational innovation processes and ICT support. In order to frame this debate this paper initially highlighted the distinction between the innovation process and the innovation product, which were proposed as two essential aspects of systems development (Bannon, 1993; Mathiassen, 1998). Crucially, a distinction between the process of shaping ICT and the type of technological product produced will further support discussion of the types of ICT support for knowledge work. Second, this paper has introduced the basic distinction between technological support for the individual and automation as opposed to support for collaboration (Schmidt and Bannon, 1992; Bannon, 1993). The introduction of this distinction was motivated by the Swan et al. (1999a) black box view of technology in the sense that ICT is promoted as a critical success factor within the cognitive model and a barrier within the community model. Although their arguments concerning the challenges of codifying and technologically embedding tacit knowledge are indeed valid, a simple distinction between two views on information technology can facilitate a deeper analysis of the two cases. The distinction between an individual and a collective perspective on ICT relates to the changing views of computer applications in terms of three eras: (1) as separate entities increasing productivity and efŽ ciency through the automation of existing manual processes, (2) as networked entities supporting collab-

Sørensen and Snis oration between professional groups and (3) as integral entities of business strategies and of global networks facilitating collaboration across organizational and national boundaries (Mathiassen, 1998). The distinction has also been cultivated from a computer science perspective, distinguishing between algorithms and interaction (Wegner, 1997). Figure 3 illustrates the positioning of the innovation processes and innovation products in cases A and B with respect to the cognitive and community models. With regard to the innovation process, the weakness of case A, which was a lack of negotiation and socially situated interaction, was the strength of case B. Had the latter process entailed one person without deep domain knowledge deŽ ning a classiŽ cation scheme, the chances are that the scheme would never have been adopted. Case A demonstrated the problems associated with a lack of community (Swan et al., 1999a) and practice (Cook and Brown, 1999) in the innovation process. This resulted in the process being abandoned altogether. Case B demonstrated the strength of a community in codifying organizational knowledge. In terms of innovation product, the aim of case A was to encode the rules governing the thermal spraying process and embed them into a tool partly automating the process. Since the process was essentially viewed exclusively as a technical process, human interference was considered negligible. In case B the aim was to produce a partly manual and partly computer-based tool, thereby supporting collaboration. Carstensen and Sørensen (1996) argued that a need for systematic support for the coordination of distributed activities could emerge from the increased complexity of work processes. In case B the classiŽ cation system reduces the complexity of distributed storage and retrieval activities. The formalizations of agreed categories and subcategories of components in instruments together with clearly deŽ ned data sets that must be entered for each stored CAD speciŽ cation have made it possible for people within or across projects to store and retrieve speciŽ cations without any signiŽ cant need for ‘expensive’ person-to-person interaction. It could be argued that, without interaction and community building, no system will successfully address the thermal spraying problem in case A. It was not a matter of Ž nding the ‘technical Ž x’ but rather of creating new situational and generic knowledge about the optimization of multiple parameters. Eventually a system may be able to support the storage and use of the principles governing the quality of this manufacturing process. Initially, however, the most effective way of addressing the problem would be community building and negotiation. We therefore argue that the expert system should be replaced by a system of experts. These experts could, in terms of knowledge exploration, exchange explicit,

Innovation through knowledge codiŽ cation

93

Table 2 Summarizing and categorizing cases A and B according to the cognitive and the community model (Swan et al., 1999a) Case A: codifying thermal spraying

Case B: classifying CAD models

Thermal Spraying Quality Assurance Project

Collaborative development of classiŽ cation scheme for CAD models

Project aims to overcome problems of quality control within the thermal spraying process

Scheme aims to support distributed storage, retrieval and reuse of CAD speciŽ cations

In the main, the organization viewed the effort as one of objectively deŽ ning and codifying the rules governing the process qualities

The ad hoc group emerged informally as a response to the need for reuse of CAD speciŽ cations when the organization migrated from paper-based design to CAD

The rule base specifying the qualitative rules affecting process quality was elicited by the expert interviewing different people in the organization, re ecting the various roles involved in thermal spraying

The classiŽ cation scheme was constructed as collaborative after-hours ‘skunk works’ over a period of several weeks characterized by many discussions and arguments

Expert system at the centre of the development effort

System of experts responsible for critical aspects of designing the codiŽ cation of the manufacturing domain in the organization

The development of a computer-based expert system technology was a critical success factor

The CAD system support for the classiŽ cation system was a minor but helpful feature. However, the printed classiŽ cation scheme was considered crucial

Project unsuccessful in providing a viable technological solution. It was concluded that, due to the individual participants’ different and con icting opinions regarding the rules and the ranking of the rules affecting the quality of the thermal spraying process, an expert system would not solve the problem at that stage

The project resulted in a classiŽ cation scheme that was subject to constant debate in the organization. Characteristically, most critique was voiced by people who had not participated in the design of the scheme and, therefore, had not had their perspective sufŽ ciently represented

tacit, individual and collective knowledge regarding the dynamics of the rule base for optimizing quality. The organizational ‘blindness’ inhibiting the view of thermal spraying as a social process could not be explained by a predominant engineering or technical culture, given that case B was also from the manufacturing domain. However, the organization in case A conceptualized the problem as related to improvements of the processes itself – an aspect that directly added value. On the other hand, case B demonstrated improvements in the coordination of the process rather than adding value directly (Carstensen and Sørensen, 1996; Schmidt and Simone, 1996). Here, relatively little direct support was provided for the design of components that were reusable in other contexts (a value adding process). Instead, the support was focused on facilitating an effective distributed ‘negotiation’ of where to place and where to look for potentially useful CAD speciŽ cations. The fact that the two cases each fall within one model for knowledge management does not imply that different conŽ gurations cannot be encountered in other circumstances. It is, for example, not obvious that the application of a community model for exploring organizational knowledge will necessarily lead to technological

support for collaboration. For example, in case A an innovation process initiating within the community model (scenario C in Figure 3) as a precursor for the codiŽ cation process could perhaps have established a more solid basis for developing a complex expert system.

Figure 3 Within Swan et al.’s (1999a) framework, which distinguished between the innovation process and the innovation product, cases A and B each match a model for knowledge management. However, two other conŽ gurations of innovation process and product can be envisioned (C and D)

94 In addition, scenario D in Figure 3 could for example be the stipulation of a simple collaboration tool, for example supporting communication using simple categorizations as discussed by Robertson et al. (2000) or providing a simple common information space for shared work activities (Schmidt and Bannon, 1992; Bannon, 1993). A more comprehensive support for collaborative activities in the form of coordination mechanisms stipulating the coordination of distributed activities would to a large extent rely on classiŽ cation structures and computer-based protocols (Carstensen and Sørensen, 1996; Schmidt and Simone, 1996). Such support would most likely not be a successful outcome of knowledge exploitation, but would rely on the collaborative processes of knowledge exploration in order for the participants to negotiate the appropriate classiŽ cations and algorithmic properties of the support. A central aspect of this negotiation concerns the allocation of functionality between humans and formalized systems as well as between computer-based and non-computerbased artefacts. In case A, these decisions were to a large extent made at the outset. The expert system would by deŽ nition embed essential rules modelling the thermal spraying process and as such automate and ‘hide’ the complexity. The purpose of the technological support was to automate the standardization of process quality. In case B the purpose of the knowledge exploration process was to uncover the assumptions, opinions and experiences of people with different professional backgrounds about products and components and express the negotiated order as a formalized classiŽ cation system in order to discuss, express and formalize the complexity. The knowledge possessed by the group was drawn out through the action of debating (Cook and Brown, 1999) or socialization (Nonaka and Takeuchi, 1995). In case B the allocation of functionality between humans and different types of manual and computer-based information technology was not determined from the start. The result could potentially have been a simple paper-based classiŽ cation scheme that mapped onto a set of directories for the storage of CAD speciŽ cations. However, the end result was a complex system with an essential paper-based part – the printed classiŽ cation scheme – as well as the complex computer-based part in the form of a database system integrated with both the CAD and material planning systems. An essential aspect of the classiŽ cation system in case B is that, although it supported collaboration and learning within and across projects, it did not stipulate this collaboration. The individual person using the system could choose when and how to classify a CAD speciŽ cation. In this respect, major aspects of the functionality reside outside the formalized system. Swan et al. (1999a) highlighted the problems of innovation supply and demand. Case B illustrates a demand

Sørensen and Snis approach, particularly in terms of the negotiation process but also to some extent in terms of the use of the classiŽ cation scheme. The ad hoc group codifying the theory of products produced by the company did so out of a perceived need. No manager told them to participate in these activities. However, it could be argued that the implicit pressure on members of the organization to use the scheme is slightly biased towards being supply driven. For example, the additional time spent classifying a CAD model would perhaps beneŽ t someone else and not necessarily the person classifying the components. The egalitarian culture in the organization meant that this was not perceived as a problem. However, it is important to distinguish between innovation process and product clearly. The classiŽ cation scheme was the result of a negotiation process amongst a small group of people exploring new knowledge. This knowledge was codiŽ ed into the classiŽ cation scheme that, with its tacit conventions for use, represented exploitation of the knowledge. The product of the innovation, the scheme with associated heuristics for the use of it, was subsequently the subject of a diffusion process within the organization. Over time the classiŽ cation scheme gradually became out of synchronization with the organizational manufacturing reality that it was created from; it was thus subject to recoding through further negotiation, critique and exploration. The initial collective theory building exercise was constantly challenged and informed by the practice of everyday use of the classiŽ cation scheme, thus illustrating the duality between knowing and doing or classiŽ cations and classifying (Bowker and Star, 1999) Given the increasing geographical and temporal distribution of work activities and the emergence of ICTs in most walks of organizational life, there is and will be an increasing need for computer-supported codiŽ ed knowledge. There will also increasingly be a need for applying ICT in the process of codiŽ cation itself. The process of coordinating, negotiating and planning work applies recursively to the coordination of work (Schmidt and Simone, 1996). Therefore, to the extent that we can reduce the complexity of negotiating where CAD models are located on a computer network, we can, through ICT, perhaps also support the negotiation of the categories we use when classifying. Communities that only exist virtually and interact through computerbased networks have little other choice. They establish principles and systems for categorizing and codifying the world they inhabit (Sørensen, 1999). For many years, a process of on-line negotiation has governed the emergence of new usenet news groups. By focusing exclusively on the community model, we may overlook opportunities for technology-supported implicit codiŽ cation by observed behavioural patterns. In case B, categories rarely used could be drawn to the back-

Innovation through knowledge codiŽ cation ground with occasional votes as to which of the infrequent ones should remain, what new categories could be suggested and which ones were no longer relevant. We could argue that the notion of classiŽ cation structures as rigidly codiŽ ed aspects of the world could be partly de-emphasized through the use of ICTs.

Conclusion People and ICT are increasingly interwoven. Addressing the relationships between people creating and managing knowledge and systems supporting, facilitating and enabling them to do so involves complex considerations and difŽ cult design choices. This paper has discussed organizational innovation through the classiŽ cation and codiŽ cation of manufacturing knowledge. Such classiŽ cation and codiŽ cation processes will, if successful, lead to the development of information artefacts. Negotiating what aspects of information systems should be formalized and embedded into information artefacts and what aspects human actors should carry out is a complex process. Furthermore, the information artefacts can be entirely physical, for example paper forms, they can be digitally embedded into ICT or they can be a complex mixture of both (Carstensen and Sørensen, 1996). This paper has analysed two cases of knowledge classiŽ cation and codiŽ cation, both of which were aimed at innovating manufacturing processes through ICT. The analysis demonstrated strength of knowledge exploration situated in a social context of shared practice as a means of providing ICTs that codify knowledge. The paper also demonstrated that, in the exploration of the conditions for providing ICT support for knowledge work, the cognitive and the community models for knowledge management based on organizational theories (Swan et al., 1999a) could greatly inform a discourse. However, the lack of proper attention to aspects of ICTs and to ICT development in the framework can be alleviated by explicitly distinguishing between innovation process and product. It is further suggested in this paper that tools for individuals or for automation were the innovation product best matching the cognitive model. Innovation products within the community model can best be characterized as ICTs for collaboration. Separating the innovation process and innovation product, the paper has argued that the choice of knowledge management model for the innovation process need not determine the type of innovation product resulting from this process in terms of individual or collective support. Engaging in a collaborative knowledge exploration process may indeed be an essential precursor for a cognitive knowledge exploitation process if the purpose is to provide ICTs that automate aspects of the work processes.

95 Faced with difŽ cult design decisions regarding support for managing knowledge, simple contingencies and classiŽ cations of possible solutions will not sufŽ ce. The decisions regarding the development of ICT support for knowledge management based on the classiŽ cation and codiŽ cation of knowledge cannot be exclusively analysed from one perspective on knowledge management. Competing perspectives can inform us about different aspects of a complex phenomenon. In an attempt to balance concerns for social and technical issues, ICT can serve a role as support technology for the management of knowledge, but Ž rm conclusions must be based on careful consideration of issues of organizing and issues related to (1) the emergent properties of different technologies, (2) the allocation of functionality between humans and systems and (3) the design of both manual and computer-based technologies. The aim of this paper has been to further some of the work on the management of knowledge within organizational theory concerning the classiŽ cation of knowledge and analyse the Ž ndings based on a view towards developing ICTs. However, more research is needed on the interrelations between discourses on knowledge management and ICT development.

Acknowledgements The authors thank all the people at Volvo Aero and Foss Electric, as well as Peter Carstensen and Henrik Borstrøm for participating in the original research project for case B. In addition, they thank Maxine Robertson for comments and for proofreading the manuscript. This research was partly funded by the European Union Area 2 Project Laboratorium for Interaction Technology at Trollhättan Uddevalla University, Sweden.

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Biographical notes Carsten Sørensen lectures Information Systems at London of Economics and Political Science, United Kingdom (is.lse.ac.uk/staff/sorensen). He holds a BSc. (Math), an MSc. (Comp.Sci) and Ph.D. (Comp.Sci & Information Systems) from Aalborg University,

97 Denmark. Carsten’s area of research is information and communication technology supporting complex work in technical domains. Currently he is researching the management of interaction in knowledge intensive Ž rms. Carsten is one of thew founding members of the Internet Project (internet.informatik.gu.se) and Laboratorium for Interaction Technology (laboratorium.htu.se) and he has through the past 12 years been afŽ liated with a number of Danish, Swedish and British institutions. Ulrika Lundh-Snis is Assitant Professor in Information Systems at Trollhättan Uddevalla University in Sweden. She holds a BSc (Inf. Syst.) from Trollhättan Uddevalla university, an MSc and an MPhil (Inf. Syst.) from Göteborg University, and is currently studying for her PhD at Göteborg University. Ulrika worked on the Internet Project (internet.informatik.gu.se) and is one of the founding members of Laboratorium for Interaction Technology (laboratorium.htu.se). Ulrika’s area of research is collaborative aspects of knowledgework in manufacturing. She is a member of the IRIS Association (iris.information.gu.se). Address for correspondence: Carsten Sørensen, Department of Information Systems, The London School of Economics and Political Science, London, UK.