JILT 2002 (2) - Petter Gottshchalk
Figure 1: The Stages of Growth Model for Knowledge Management Technology Stages of IT support in knowledge management are useful to identify the current situation as well as to plan for future applications in the firm. Let us look more closely at each stage in Figure 1 : I. End-user tools are made available to knowledge workers. At the simplest stage, this means a capable networked PC on every desk or in every briefcase, with standardized personal productivity tools (word processing, presentation software) so that documents can be exchanged easily throughout a company. More complex and functional desktop infrastructures can also be the basis for the same types of knowledge support. Stage I is recognized by widespread dissemination and use of end-user tools among knowledge workers in the company. For example, lawyers in a law firm will at this stage use word processing, spreadsheet, legal databases, presentation software, and scheduling programs. II. Information about who knows what is made available to all people in the firm and to selected outside partners. Search engines should enable work with a thesaurus, since the terminology in which expertise is sought may not always match the terms the expert uses to classify that expertise. Here we find the cartographic school of knowledge management ( Earl, 2001 ), which is concerned with mapping organizational knowledge. It aims to record and disclose who in the organization knows what by building knowledge directories. Often called 'yellow pages -', the principal idea is to make sure knowledgeable people in the organization are accessible to others for advice, consultation, or knowledge exchange. Knowledge-oriented directories are not so much repositories of knowledge-based information as gateways to knowledge, and the knowledge is as likely to be tacit as explicit. One starting approach at Stage II is to store curriculum vitae (CV) for each knowledge worker in the firm. Areas of expertise, projects completed and clients helped may over time expand the CV. For example, a lawyer in a law firm works on cases for clients using different information sources that can be registered on yellow pages in terms of an intranet. III. Information from knowledge workers is stored and made available to all people in the firm and to selected outside partners. Here data mining techniques can be applied to find relevant information and combine information in data warehouses. On a broader basis, search engines are web browsers and server software that work with a thesaurus, since the terminology in which expertise is sought may not always match the terms the expert uses to classify that expertise. One starting approach at Stage III is to store project reports, notes, recommendations and letters from each knowledge worker in the firm. Over time, this material will grow fast, making it necessary for a librarian or a chief knowledge officer (CKO) to organize it. In a law firm, all client cases will be classified and stored in databases using software such as Lotus Notes. IV. Information systems solving knowledge problems are made available to knowledge workers and solution seekers. Artificial intelligence is applied in these systems. For example, neural networks are statistically oriented tools that excel at using data to classify cases into one category or another. Another example is expert systems that can enable the knowledge of one or a few experts to be used by a much broader group of workers who need the knowledge. Expert system is an example of knowledge management technology at Stage IV. According to Curtis and Cobham ( 2002 ), the short answer is that an expert system is a computerized system that performs the role of an expert or carries out a task that requires expertise. In order to understand what an expert system is, then, it is worth paying attention to the role of an expert and the nature of expertise. It is then important to ascertain what types of expert and expertise there are in business and what benefits will accrue to an organization when it develops an expert system. For example, a doctor having a knowledge of diseases comes to a diagnosis of an illness by reasoning from information given by the patient's symptoms and then prescribes medication on the basis of known characteristics of available drugs together with the patient's history. The lawyer advises the client on the likely outcome of litigation based on the facts of the particular case, an expert understanding of the law and a knowledge of the way the courts work and interpret this law in practice. The accountant looks at various characteristics of a company's performance and makes a judgement as to the likely state of health of that company ( Curtis and Cobham, 2002 ). All of these tasks involve some of the features for which computers traditionally have been noted ? performing text and numeric processing quickly and efficiently ? but they also involve one more ability: reasoning. Reasoning is the movement from details of a particular case and knowledge of the general subject area surrounding that case to the derivation of conclusions. Expert systems incorporate this reasoning by applying general rules in an information base to aspects of a particular case under consideration ( Curtis and Cobham, 2002 ). When companies want to use knowledge in real-time, mission-critical applications, they have to structure the information base for rapid, precise access. A web search yielding hundreds of documents will not suffice when a customer is waiting on the phone for an answer. Representing and structuring knowledge is a requirement that has long been addressed by artificial intelligence researchers in the form of expert systems and other applications. Now their technologies are being applied in the context of knowledge management. Rule-based systems and case-based systems are used to capture and provide access to customer service problem resolution, legal knowledge, new product development knowledge, and many other types. Although it can be difficult and labour-intensive to author a structured knowledge base, the effort can pay off in terms of faster responses to customers, lower cost per knowledge transaction, and lessened requirements for experienced, expert personnel ( Grover and Davenport, 2001 ). Expert systems are at stage IV. Stewart ( 1997 ) argues for stage II by stating that knowledge grows so fast that any attempt to codify it all is ridiculous; but the identities of in-house experts change slowly. Corporate yellow pages should be easy to construct, but it's remarkable how few companies have done it. A simple system that connects inquirers to experts save time, reduces error and guesswork, and prevents the reinvention of countless wheels. What could be stored at Stage III, according to Stewart ( 1997 ), are lessons learned and competitor intelligence. A key way to improve knowledge management is to bank lessons learned - in effect, checklists of what went right and wrong, together with guidelines for others undertaking similar projects. In the area of competitor intelligence, companies need to organize knowledge about their suppliers, customers, and competitors. 7. Application of the Stages of Growth Model Information technology can be applied at four different levels to support knowledge management in an organization. At the first level, end user tools are made available to knowledge workers. At the second level, information on who knows what is made available electronically. At the third level, some information representing knowledge is stored and made available electronically. At the fourth level, information systems capable of simulating human thinking are applied in the organization. These four levels are illustrated in Table 1 , where they are combined with knowledge management tasks. The entries in the figure only serve as examples of current systems.
Table 1: Examples of IS/IT at different Knowledge Management Stages. When the Stages of Growth model is applied to the two classifications of knowledge presented earlier, we get the Table 2 below. For example, a law firm can have procedural knowledge at the core, advanced and innovative levels. Also, a law firm can have administrative knowledge not only at the core level, although 'nuts and bolts' knowledge appears to be very much like core knowledge. For example, a law firm can be extremely creative in exploring client relationships using an extranet.
Table 2: Knowledge Management Matrix. The knowledge management matrix can first be used to identify the current IS/IT that support knowledge management in the firm as illustrated in Table 3 .
Table 3: Knowledge Management Matrix for the Current IS/IT Situation. The placement of items within the various categories may appear at times to be inconsistent. For example, why is the Internet considered to be advanced knowledge? Some may argue that it is core knowledge as Internet access doesn't differentiate a law firm from its competitors any more than does e-mail. The reason for the Internet categorisation is the fact that the Internet provides access to advanced knowledge rather than core knowledge. Each categorisation expresses knowledge access rather than technology sophistication. Now the knowledge management matrix can be applied to identify future IS/IT as illustrated in Table 4 . The systems not only serve as examples; they also illustrate that it is possible to find systems than can support all combinations of knowledge categories and knowledge levels. This figure illustrates both current and future applications of information systems and information technology, enabling a diagnosis of both current and future stage of growth for knowledge management technology in a law firm. Future applications are in italics.
Table 4: Knowledge Management Matrix for Desired IS/IT Situation. Let us look at one system in a most demanding location, innovative-analytical knowledge. There we find expert system. According to Susskind ( 2000 , p.163), six kinds of expert systems can play an important role in law firms in the future:
Software and systems suitable for knowledge management in a law firm can now be identified using the knowledge management matrix. In Table 5 , examples of software to support systems in Table 4 are listed.
Table 5: Knowledge Management Matrix for Software Supporting Desired IS/IT Situation. Let us look at one example in Table 5 . Knowledger is listed as a potential software in the innovative-analytical knowledge location. This is an ambitious location for a software product that has yet to demonstrate its real capabilities in knowledge firms. According to the vendor Knowledge Associates, Knowledger 3.0 is a complete knowledge management software that can be integrated with other systems in the firm. Knowledger is web-based and supports the firm in categorizing internal and external information, as well as linking incoming information to existing information. Let us look at one more application in a most demanding location, innovative-analytical knowledge. There we find Summation. Summation is a system for document handling for use in large court cases. In the large Norwegian Balder court case of 2001, the Thommessen Krefting Greve Lund (TKGL) law firm used Summation. The Balder case is a dispute between Exxon and Smedvig about the rebuilding of an offshore vessel costing 3 billion Norwegian kroner. TKGL had more than 2500 binders when the court case started in the city of Stavanger. All these documents were scanned into a database for use by Summation. When lawyers from TKGL present material in court, they submit it from their laptops. When new information emerges in court, it is registered in Summation. When TKGL lawyers are to trace technical and financial developments for Balder, they make a search in the Summation database. Another law firm is also using Summation. The law firm Bugge Arentz-Hansen Rasmussen (BA-HR) has the task of locating money left by the late ship owner Jahre. The money is expected to be found in banks in tax havens. The hunt for Jahre funds has been going on for almost a decade, and BA-HR has developed a large Summation database enabling BA-HR lawyers to present important information in the court in the city of Drammen. A third example of Summation use can be found in the US. The Justice Department used Summation in its legal struggle with Microsoft. According to Summation Legal Technologies, Summation helped the Justice's lead prosecutor, David Boies, piece together the most damaging information against Microsoft. In presenting its defence, which ended on February 26, Microsoft relied more than Justice did on a low-tech overhead projector. Summation and Knowledger are interesting examples of software for knowledge management in law firms because they are applications at Stage IV of the Stages of Growth model. The Stages of Growth model can be applied to the knowledge management matrix as illustrated in Table 6 .
Table 6: Knowledge Management Matrix applied to Stages of Growth. IT for administrative core and advanced knowledge as well as IT for declarative core and advanced knowledge is mainly end-user tools at Stage I. IT for administrative and declarative innovative knowledge is mainly for who knows what at Stage II. IT for advanced analytical knowledge is mainly for what they know at Stage III, while IT for innovative analytical knowledge is mainly for what they think at Stage IV. The classification of each of the twelve matrix elements in Table 6 can be challenged. The main framework, however, should be agreeable. The main idea says that when a law firm moves from the upper-left corner in the knowledge management matrix to the lower-right corner in the matrix, then the firm evolves through stages of growth in the use of knowledge management technology. 8. Legal Grid and Stages of Growth Model From an IT-perspective, Susskind ( 2000 ) has illustrated how law firm focus can vary as illustrated in Figure 2 . He defines a horizontal axis from technology focus to knowledge focus, and a vertical axis from firm focus to client focus. He calls it the legal grid. In each quadrant, IT applications can be identified. Office support systems are technology- oriented applications internal to the firm. Knowledge management systems are knowledge- oriented applications internal to the firm. Legal web advice is knowledge-oriented applications focusing on clients, while customer relationship management is technology-oriented applications focusing on clients.
Figure 2: The Legal Grid for Applications of IT in Law Firms. Susskind ( 2000 ) has illustrated how the focus has shifted in law firms as illustrated in Figure 3 . Until 1995, law firms were concentrating on office support systems such as accounting and other administrative systems. In 1995, knowledge management became the focus of attention. Soon customer relationship management will emerge, and so will legal web advice. An interesting aspect of this illustration is the link between different parts. One important link is the need for KMS to enable both CRM and LWA.
Figure 3: Changing Focus Over Tme for Applications of IT in Law Firms. The Stages of Growth model as proposed in this article, is mainly concentrating in the bottom-left quadrant of knowledge management. End-user tools is the starting point for knowledge management, before advancing into information about who knows what. Knowledge management becomes even more advanced in the legal grid quadrant when information from knowledge workers is stored in computer systems. The most advanced stage is occurring when knowledge management in the bottom-left quadrant is at Stage IV by a law firm having information systems solving knowledge problems. As illustrated in Figure 3 , the knowledge management quadrant is very much the attention of this decade. Attention will expand into client-related systems in a few years. This is interesting in the Stages of Growth model perspective, as LWA will be dependent on KMS to be successful. Dependence of LWA on KMS is visible in anecdotal evidence from current online legal service providers. In Norway, legal web advisors such as Legaliz.no, AdvokatOnline.no, Jusstorget.no and Internettadvokaten.no all struggle to survive. These actors have neither a backbone system in terms of internal knowledge management systems nor backbone staff in terms of a minimum number of lawyers on which web services can be updated and renewed. An existing law firm in Norway with typically 50-150 lawyers will have both the knowledge and the resources for knowledge management to develop an internal knowledge management system that can be the foundation for legal web advice. Figure 4 illustrates how the Stages of Growth model can be linked to the Legal Grid.
Figure 4: Stages of Growth Linking Knowledge Management and Legal Web Advice. 9. Validation of the Stages of Growth Model The next stage in this research will be to validate the model. Empirical validation of the Stages of Growth model can be carried out though a survey using a questionnaire as listed in the Appendix . In the first part of the survey instrument in the Appendix , there are four research constructs defined, one for each stage. Each construct is measured through a multiple-item scale. Each scale has five items, where the fifth item is a summary item. For each responding law firm, the average value for each level can be calculated. For the whole sample, statistical difference tests such as the t-test can be applied to evaluate whether responding law firms report significant differences between stages. Empirical validation of the Stages of Growth model will be successful if responding law firms have significantly lower scores at higher levels. In the second part of the survey instrument in the Appendix , the four stages of growth are described in terms of benchmark variables. Benchmark variables indicate the theoretical characteristics at each stage of growth ( King and Teo, 1997 ). For example, firms at Stage I can theoretically be expected to conform to values of benchmark variables listed under Stage I. However, this does not mean that it is not possible for firms at Stage I to have values of benchmark variables applicable to other stages. Rather, it means that the values of benchmark variables indicate the most likely theoretical characteristics applicable at each stage of integration as indicated in Table 7 .
Table 7: Typology of Evolutionary Stages of Knowledge Management Technology. There are a total of twenty-one benchmark variables in Table 7 . Twelve benchmark variables are concerned with IT in KM, the next four benchmark variables (no. 13-16) are concerned with IT management, while the remaining five (no. 17-21) are concerned with knowledge management in general. Benchmark variables in Table 7 indicate the theoretical characteristics at each stage of growth. The problem with this approach is that all indicators of a stage may not be present in an organisation, which makes it difficult to place the organisation in any specific stage. Unfortunately, IT factors do not lend themselves to precise Guttman scaling techniques. Guttman scaling is also sometimes known as cumulative scaling or scalogram analysis . The purpose of Guttman scaling is to establish a one-dimensional continuum for a concept to measure. We would like a set of items or statements so that a respondent who agrees with any specific question in the list will also agree with all previous questions. This is the ideal for a stage model - or for any progression. By that we mean that it is useful when one progresses from one state to another state in a manner so that if one is in the later/higher stage, it also indicates that one has all the features of the earlier stage ( Trochim, 2002 ). In the third part of the survey instrument in the Appendix , the four stages of growth are extensively described, enabling respondents to make an overall judgement of knowledge management technology stage in the firm. In the fourth part of the survey instrument, a validation check of the paths of evolution is conducted. Respondents are asked to indicate the duration spent at each stage of growth. This is to ensure that respondents do think about paths of evolution ( King and Teo, 1997 ). The duration (number of years) spent at each stage is also measured in the questionnaire. In the fifth part of the survey instrument, knowledge-sharing perceptions and reward perceptions are measured as defined by Hunter and Beaumont ( 2002 ). This is done to evaluate a theoretical proposition that higher stages of growth will have higher knowledge-sharing perceptions and higher reward perceptions. In the sixth and final part of the survey instrument, strategy and responsibility questions are applied to identify intentions and focus based on content analysis of responses. This is done to evaluate a theoretical proposition that higher stages of growth will be associated with more IT and KM focused strategy statements as well as more IT and KM executives ( Hunter and Beaumont, 2002 ). The desired informant for this proposed survey instrument is the chief executive officer (CEO) who can be the managing partner or the managing director in a law firm. A Stages of Growth model is proposed to understand the stage at which at law firm is found concerning applications of information technology in knowledge management. Four stages are defined, and a law firm can use the model to develop a strategy for implementing technology at higher stages in the model. Alavi, M and Leidner, D E (2001), 'Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues', MIS Quarterly , 25 (1), pp.107-136. Becker, W M, Herman, M F, Samuelson, P A and Webb, A P (2001), 'Lawyers Get Down to Business', The McKinsey Quarterly , 2001 (2), pp.45-55. Curtis, G and Cobham, D (2002), Business Information Systems: Analysis, Design and Practice , UK: Prentice Hall. Davenport, T H and Prusak, L (2000), Working Knowledge , USA: Harvard Business School Press. Earl, M J (2001), 'Knowledge Management Strategies: Toward a Taxonomy', Journal of Management Information Systems, 18 (1), pp.215-233. Edwards, D L and Mahling, D E (1997), 'Toward Knowledge Management Systems in the Legal Domain', Proceedings of the International ACM SIGGROUP Conference on Supporting Group Work Group '97 , USA: The Association of Computing Machinery ACM, pp.158-166. Fahey, L and Prusak, L (1998), 'The Eleven Deadliest Sins of Knowledge Management', California Management Review , Spring, pp.9-21. Galanter, M and Palay, T (1991), Tournament of Lawyers, The Transformation of the Big Law Firm , USA: The University of Chicago Press. Gottschalk, P (2002), Knowledge Management through Information Technology , Bergen, Norway: Fagbokforlaget publishing, < http://www.fagbokforlaget.no >. Grover, V and Davenport, T H (2001), 'General Perspectives on Knowledge Management: Fostering a Research Agenda', Journal of Management Information Systems (JMIS), 18 (1), pp.5-21. Hitt, M A, Bierman, L, Shimizu, K and Kochhar, R (2001), 'Direct and Moderating Effects of Human Capital on Strategy and Performance in Professional Service Firms: A Resource-based Perspective', Academy of Management Journal , 44 (1), pp.13-28. Hunter, L and Beaumont, P (2002), 'Knowledge Management Practice in Scottish Law Firms', Human Resource Management Journal , 12 (2), pp.4-21. King, WR and Teo, TSH (1997), 'Integration Between Business Planning and Information Systems Planning: Validating a Stage Hypothesis', Decision Sciences , 28 (2), pp.279-307. Montana, J C (2000), 'The Legal System and Knowledge Management ', The Information Management Journal , July, pp.54-57. Mountain, D (2001), 'Could New Technologies Cause Great Law Firms to Fail?', Journal of Information, Law & Technology (JILT), Issue 2001, (1) pages.< http://elj.warwick.ac.uk/jilt/01-1/mountain.html >. Nahapiet, J and Ghoshal, S (1998), 'Social Capital, Intellectual Capital, and the Organizational Advantage', Academy of Management Review , 23 (2), pp.242-266. Stewart, T A (1997), Intellectual Capital: The New Wealth of Organizations , UK: Nicholas Brealy Publishing. Susskind, R (2000), Transforming the Law , UK: Oxford University Press. Tiwana, A (2000), The Knowledge Management Toolkit ? Practical Techniques for Building a Knowledge Management System , USA: Prentice Hall. Trochim (2002), < http://trochim.human.cornell.edu/kb/scalgutt.htm >. Knowledge Management Technology Survey What is your job title? ____________________________________________________ How many years have you been with the firm? ______years How many persons work in the firm? ______persons How many lawyers work in the firm? ______persons What is the total income budget for the firm this year? ______mill. NOK What is the total IT budget for the firm this year? ______mill. NOK How many persons work in the IT function in the firm? ______persons
For each of the following statements, please place one check mark () besides the description that most closely fits the firm. Please choose only one response for each numbered statement. (Even though more than one response may seem appropriate, please select the best statement for the firm). Please also note that none of the descriptions are inherently good or bad. 1. The implementation of information technology for knowledge management is primarily triggered by: ? ( ) consideration of individual lawyer's needs ? ( ) consideration of the organization's needs ? ( ) consideration of the organization's goals ? ( ) the need to automate lawyers' work 2. Please indicate the frequency of top management's participation in information technology planning for knowledge management: ? ( ) almost always ? ( ) frequent ? ( ) infrequent ? ( ) seldom 3. Please indicate the frequency of user participation in information technology planning for knowledge management: ? ( ) almost always ? ( ) frequent ? ( ) infrequent ? ( ) seldom 4. The principal contribution from information technology in knowledge management is: ? ( ) improved efficiency of individual lawyer's work ? ( ) improved effectiveness of individual lawyer's work ? ( ) improved effectiveness of the firm ? ( ) improved competitiveness of the firm 5. During information technology planning, how frequent is the impact of new knowledge management technologies assessed? ? ( ) almost always ? ( ) frequent ? ( ) infrequent ? ( ) seldom 6. In applying information technology to support knowledge management, we have the following main focus: ? ( ) making information technology available to lawyers ? ( ) reorganizing the firm for knowledge sharing ? ( ) creating a culture for knowledge development ? ( ) replacing lawyers by information technology 7. Please indicate the most dominating statement about knowledge management technology: ? ( ) Information technology enables me to distribute information to my colleagues ? ( ) Information technology enables me to collect information created by my colleagues ? ( ) information technology enables me to produce comprehensive documentation ? ( ) information technology enables me to concentrate on interesting work 8. The most critical success factor for information technology in knowledge management is: ? ( ) availability of PCs and networks ? ( ) culture and incentives to share knowledge ? ( ) quality and quantity of available information in databases ? ( ) availability of artificial intelligence, such as expert systems and intelligent agents 9. Please indicate the main 'philosophy' for knowledge management technology: ? ( ) our lawyers enjoy independence in time and space, by working when they like (day or night) and where they like (office, home, summerhouse) ? ( ) our firm is a knowledge community of people with a common interest, problem and experience, designed and maintained for a business purpose ? ( ) our clients are satisfied with our work, they have trust and confidence in our professional knowledge ? ( ) we help our clients solve their problems themselves by making expert knowledge available 10. Please indicate the dominating strategy for knowledge management technology: ? ( ) tool strategy of enabling lawyers to use PCs ? ( ) stock strategy of storing whatever documents that are produced in the firm ? ( ) flow strategy of only storing documents that will be used again in work processes ? ( ) growth strategy of only storing documents that are related to legal work where we have little experience 11. Presently, the main task of information technology in knowledge management is: ? ( ) distributing knowledge ? ( ) sharing knowledge ? ( ) capturing knowledge ? ( ) applying knowledge 12. Presently, information technology in knowledge management mainly exists for the purpose of: ? ( ) facilitating administrative work processes ? ( ) providing access to information more efficiently ? ( ) sharing information more effectively ? ( ) automating work done by lawyers 13. The information technology function is primarily viewed as: ? ( ) supplier of PCs and end user tools ? ( ) developer of technical infrastructure and applications ? ( ) a resource making information available ? ( ) supplier of systems that automate legal work 14. The primary role of the information technology manager is: ? ( ) an information technology expert who knows PCs and IT tools ? ( ) a functional administrator responsible for providing support ? ( ) an information resources manager ? ( ) a knowledge management systems expert 15. The performance criteria for the information technology function are its: ? ( ) operational efficiency and cost minimization ? ( ) contribution to business strategy implementation ? ( ) contribution to knowledge strategy implementation ? ( ) long-term impact on the organization 16. Please indicate the frequency of the information technology manager's participation in business strategy planning: ? ( ) almost always ? ( ) frequent ? ( ) infrequent ? ( ) seldom 17. We are in the business of providing legal advice: ? ( ) based on efficiency of our lawyers ? ( ) based on availability of our lawyers ? ( ) based on effectiveness of our lawyers ? ( ) based on expert knowledge of our lawyers 18. Knowledge management has the following main effect: ? ( ) reduced dependence on individual lawyer's knowledge ? ( ) effective application of current knowledge ? ( ) development of new knowledge ? ( ) improved client performance 19. Knowledge management has the following priority in our business strategy: ? ( ) first priority ? ( ) second priority ? ( ) third priority ? ( ) fourth priority 20. Knowledge management is at the top management agenda: ? ( ) every day ? ( ) every week ? ( ) every month ? ( ) every year 21. Knowledge management has the following priority in our marketing strategy: ? ( ) first priority ? ( ) second priority ? ( ) third priority ? ( ) fourth priority 22. The information technology manager is ____level(s) below the managing director. 23. The information technology manager has been with the firm for ____years. 24. The knowledge manager is ____level(s) below the managing director. 25. The knowledge manager has been with the firm for ____years. Please indicate with one check mark () the description that most closely fits your current projects for information technology to support knowledge management in the firm: ? ( ) End-user tools will be made available to lawyers. This means a capable networked PC on every desk or in every briefcase, with standardized personal productivity tools (word processing, presentation software) so that documents can be exchanged easily throughout a company. A widespread dissemination and use of end-user tools among knowledge workers in the company is to take place. ? ( ) Information about who knows what will be made available to lawyers. It aims to record and disclose who in the organization knows what by building knowledge directories. Often called 'yellow pages', the principal idea is to make sure knowledgeable people in the organization are accessible to others for advice, consultation, or knowledge exchange. Knowledge-oriented directories are not so much repositories of knowledge-based information as gateways to knowledge. ? ( ) Information from lawyers will be stored and made available to colleagues. Here data mining techniques will be applied to find relevant information and combine information in data warehouses. One approach is to store project reports, notes, recommendations and letters from each lawyers in the firm. Over time, this material will grow fast, making it necessary for a librarian or a chief knowledge officer (CKO) to organize it. ? ( ) Information systems solving knowledge problems will be made available to lawyers. Artificial intelligence will be applied in these systems. For example, neural networks are statistically oriented tools that excel at using data to classify cases into one category or another. Another example is expert systems that can enable the knowledge of one or a few experts to be used by a much broader group of lawyers who need the knowledge. As far back as you can recall, please indicate below the evolution of information technology projects for knowledge management in the firm in terms of the duration spent in each type of information technology projects, and the reasons for changing from the previous type of knowledge management technologies. Please use the terms 'not applicable' or 'NA' beside any type of information technology projects that the firm did not experience.
Please describe the firm's business strategy in one sentence:____________________ _____________________________________________________________________ Please describe the firm's knowledge strategy in one sentence:___________________ _____________________________________________________________________ Please describe the firm's information technology strategy in one sentence:_________ _____________________________________________________________________ Please describe the firm's human resources strategy in one sentence:______________ _____________________________________________________________________ Which function in the firm is responsible for knowledge management?________function Which function in the firm is responsible for IT management?________function |
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