Stages of Knowledge Management Technology: A Comparison of Law Firms in Norway and Australia

In this paper, information technology support for knowledge management is linked to stages of growth, using a stages of growth model consisting of four stages. This model is used to compare how the law firms in Norway and Australia differ as they move through various stages of growth in their application of knowledge management technology over time. The model is specifically appropriate to law firms where knowledge of professional experts is a core asset, and the careful management of this asset has special importance. INTRODUCTION Stages of growth models have been used widely in both organizational research and information technology management research. According to King and Teo (1997), these models describe a wide variety of phenomena the organizational life cycle, product life cycle, biological growth, etc. These models assume that predictable patterns (conceptualized in terms of stages) exist in the growth of organizations, the sales levels of products, and the growth of living organisms. These stages are (1) sequential in nature, (2) occur as a hierarchical progression that is not easily reversed, and (3) involve a broad range of organizational activities and structures. Benchmark variables are often used to indicate theoretical characteristics in each stage of growth. A one-dimensional continuum is established for each benchmark variable. The measurement of benchmark variables can be carried out using Guttman scales (FrankfortNachmias and Nachmias, 2002; Nunnally and Bernstein 1994). Guttman scaling is a cumulative scaling technique based on ordering theory that suggests a linear relationship between the elements of a domain and the items on a test. In this paper, a four-stage model for the evolution of information technology support for knowledge management is used to compare how the law firms in Norway and Australia differ as they move through various stages of growth in their application of knowledge management technology over time. The model is specifically appropriate to law firms where knowledge of professional experts is a core asset, and the careful management of this asset has special importance (Barton et al., 2002a, 2002b; Becker et al., 2001; Disterer, 2001; Edwards and Mahling, 1997; Galanter and Palay, 1991; Hunter et al., 2002; Montana, 2000; Mountain, 2001; Susskind, 2000). THE KNOWLEDGE MANAGEMENT TECHNOLOGY STAGE MODEL Various multistage models have been proposed for organizational evolution over time. These models differ in the number of stages. For example, Nolan (1979) introduced a model with six stages for IT maturity in organizations, which later was expanded to nine stages. Earl (2000) suggested a stages of growth model for evolving the e-business, consisting of the following six stages: external communication, internal communication, e-commerce, e-business, e-enterprise, and transformation. Each of these models identifies certain characteristics that typify firms in different stages of growth. Among these multistage models, models with four stages seem to have been proposed and tested most frequently (King and Teo, 1997). The knowledge management technology (KMT) stage model consists of four stages. The first stage is general IT support for knowledge workers. This includes word processing, spreadsheets, and email. The second stage is information about knowledge sources. An information system stores information about who knows what within the firm and outside the firm. The system does not store what they actually know. A typical example is the company intranet. The third stage is information representing knowledge. The system stores what knowledge workers know in terms of information. A typical example is a database. The fourth and final stage is information processing. An information system uses information to evaluate situations. A typical example here is an expert system. The contingent approach to firm performance implies that Stage I may be right for one firm, while Stage IV may be right for another firm. Some firms will evolve over time from Stage I to higher stages as indicated in Figure 1. The time axis ranging from 1990 to 2020 in Figure 1 suggests that it takes time for an individual firm and a whole industry to move through all stages. As an example applied later in this paper, the law firm industry is moving slowly in its use of information technology. Stages of IT support in knowledge management are useful for identifying the current situation as well as planning for future applications in the firm. Each stage can be labeled as follows: • Stage I can be labeled end-user-tools or people-to-technology as information technology provides knowledge workers with tools that improve personal efficiency. • Stage II can be labeled who-knows-what or people-to-people as knowledge workers use information technology to find other knowledge workers. • Stage III can be labeled what-they-know or people-to-docs as information technology provides knowledge workers with access to information that is typically stored in documents. Examples of documents are contracts and agreements, reports, manuals and handbooks, business forms, letters, memos, articles, drawings, blueprints, photographs, e-mail and voice mail messages, video clips, script and visuals from presentations, policy statements, computer printouts, and transcripts from meetings. • Stage IV can be labeled how-they-think or people-to-systems where the system is intended to help solve a knowledge problem. 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.idea-group.com IDEA GROUP PUBLISHING This conference paper appears in the book, Innovations Through Information Technology, edited by Mehdi Khosrow-Pour. Copyright © 2004, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. 148 2004 IRMA International Conference Copyright © 2004, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Information technology can be applied at four different levels to support knowledge management in an organization, according to the proposed Stages of Growth. 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. One reason for Stage III emerging after Stage II is the personalization strategy versus the codification strategy. The individual barriers are significantly lower with the personalization strategy, because the individual professional maintains the control through the whole knowledge management cycle. According to Disterer (2001), the individual is recognized as an expert and is cared for. Knowledge management strategies focusing on personalization could be called communication strategies, because the main objective is to foster personal communication between people. Core IT systems with this strategy are yellow pages (directories of experts, who-knows-what systems, people finder database) that show inquirers who they should talk to regarding a given topic or problem. The main disadvantages of personalization strategies are a lack of standards and the high dependence on communication skills and the will of the professionals. Such disadvantages make firms want to advance to Stage III. in Stage III, independence in time among knowledge suppliers and knowledge users is achieved (Disterer, 2002). BENCHMARK VARIABLES In Table 2, the four stages of growth for knowledge management technology are described in terms of benchmark variables. Benchmark variables indicate the theoretical characteristics in each stage of growth (King and Teo 1997). For example, firms in 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 impossible for firms in 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 in each stage of integration as indicated in Table 2. There are a total of ten benchmark variables in Table 2. Six benchmark variables (1-6) are concerned with IT in KM; the remaining four benchmark variables (7-10) are concerned with IT management. Benchmark variables in Table 2 indicate theoretical characteristics that commonly occur together. Sabherwal and Chan (2001) label this a configuration, which is defined as any multidimensional constellation of conceptually distinct characteristics that commonly occur together. Configurations take a step beyond the traditional contingency theoretic view by using a holistic rather than a reductionistic stance. They offer richer insights by focusing on parsimonious and relatively homogeneous groups rather than diverse concepts. SURVEY OF LAW FIRMS For survey of law firms in Norway largest law firms were selected by identifying all law firms that had at least five lawyers in the firm. This procedure resulted in a total of 102 law firms. It was possible to obtain email addresses for managing directors / chief executive officers in 95 of these law firms by contacting the firms. Most law firms in Norway are small. Because knowledge management technology for sharing information is dependent on a minimum number of lawyers to make sense, only law firms with a minimum of five lawyers were selected for this survey. Questionnaires were prepared and sent to the chief executive officer (CEO) in each firm, with two follow-ups about one week and two weeks after the date of the initial mailings. Five firms declined participation citing that the questionnaire was too long. Useable responses were returned by 19 firms, providing a response rate of 20%. For the survey in Australia questionnaires were mailed to a total of 500 firms of which 47 firms responded, representing a response rate of 9%. COMPARISON OF NORWEGIAN AND AUSTRALIAN LAW FIRMS Having collected survey data in both Norway and Australia, we are now able to make comparisons between the two countries. From previous studies we know that Australia and Norway both have similarities and differences in business in terms of information technology applications. Table 3 lists characteristics of respondents in Australia and Norway. Participating law firms in Australia were larger than participating firms in Norway. The partner ration is larger in Norway than in Australia. The IT budget in Australia has a larger fraction of the firm’s income budget. Most Norwegian law firms are in Stage III, while most Australian law firms are in Stage I. As a consequence of many Australian law firms found in Stage I, many Australian law firms (56.8%) report no path of evolution as listed Figure 1: The Stages of Growth Model for Knowledge Management Technology Stages of Growth for Knowledge Management Technology Stage IV HOW THEY THINK Stage III WHAT THEY KNOW Stage II WHO KNOWS WHAT Stage I END USER TOOLS