Understanding the characteristics of various ba and the relationship with the modes of knowledge creation is important to enhancing organizational knowledge creation.
For example, the use of IT capabilities in cyber ba is advocated to enhance the ef- ficiency of the combination mode of knowledge creation. Data warehousing and data mining, document management systems, software agents and intranets may be of great value in cyber ba. Considering the flexibility of modern IT, other forms of organizational ba and the corresponding modes of knowledge creation can be enhanced through the use of various forms of information systems. For example, information systems designed for support or collaboration, coordination and com- munication processes as a component of the interacting ba, can facilitate teamwork and thereby increase an individual’s contact with others.
Electronic mail and group support systems have the potential of increasing the number of weak ties in organizations. This, in turn, can accelerate the growth of knowledge creation. Intranets enable exposure to greater amounts of online organi- zational information, both horizontally and vertically, than may previously have been the case. As the level of information exposure increases, the internalization mode of knowledge creation, wherein individuals make observations and interpretations of information that result in new individual tacit knowledge, may increase. In this role, an intranet can support individual learning (conversion of explicit knowledge to personal tacit knowledge) through provision of capabilities such as computer simulation (to support learning-by-doing) and smart software tutors.
Computer-mediated communication may increase the quality of knowledge creation by enabling a forum for constructing and sharing beliefs, for confirming consensual interpretation and for allowing expression of new ideas. By providing an extended field of interaction among organizational members for sharing ideas and perspec- tives, and for establishing dialogue, information systems may enable individuals to arrive at new insights and/or more accurate interpretations than if left to decipher information on their own.
Although most information repositories serve a single function, it is increasingly common for companies to construct an internal portal so that employees can access multiple repositories and sources from one screen. It is also possible, and increasingly popular, for repositories to contain not only information, but also pointers to experts within the organization on key knowledge topics. It is also feasible to combine stored information with lists of the individuals who contributed the knowledge and could provide more detail or background on it (Grover & Davenport, 2001).
According to Grover and Davenport (2001), firms increasingly view attempts to transform raw data into usable knowledge as part of their knowledge management initiatives. These approaches typically involve isolating data in a separate “warehouse”
for easier access, and the use of statistical analysis or data mining and visualiza- tion tools. Since their goal is to create data-derived knowledge, the initiatives are increasingly addressed as a part of knowledge management. Some vendors have
already begun to introduce e-commerce tools in this area. They serve to customize the menu of available knowledge to individual customers, allowing sampling of information before buying and carrying out sales transactions for knowledge pur- chases. Online legal services are typical examples where clients can sample legal information before buying a lawyer’s time.
For knowledge creation there is the current emergence of idea-generation software.
Idea-generation software is designed to help stimulate a single user or a group to produce new ideas, options and choices. The user does all the work, but the software encourages and pushes, something like a personal trainer. Although idea-generation software is relatively new, there are several packages on the market. IdeaFisher, for example, has an associative lexicon of the English language that cross-references words and phrases. These associative links, based on analogies and metaphors, make it easy for the user to be fed words related to a given theme. Some software packages use questions to prompt the user toward new, unexplored patterns of thought. This helps users to break out of cyclical thinking patterns and conquer mental blocks.
Knowledge Storage and Retrieval
According to Alavi and Leidner (2001), empirical studies have shown that while organizations create knowledge and learn, they also forget (i.e., do not remember or lose track of the acquired knowledge). Thus, the storage, organization and retrieval of organizational knowledge, also referred to as organizational memory, constitute an important aspect of effective organizational knowledge management. Organiza- tional memory includes knowledge residing in various component forms, includ- ing written documentation, structured information stored in electronic databases, codified human knowledge stored in expert systems, documented organizational procedures and processes and tacit knowledge acquired by individuals and networks of individuals.
Advanced computer storage technology and sophisticated retrieval techniques, such as query languages, multimedia databases and database management systems, can be effective tools in enhancing organizational memory. These tools increase the speed at which organizational memory can be accessed.
Groupware enables organizations to create intra-organizational memory in the form of both structured and unstructured information and to share this memory across time and space. IT can play an important role in the enhancement and expansion of both semantic and episodic organizational memory. Semantic memory refers to general, explicit and articulated knowledge, whereas episodic memory refers to context-specific and situated knowledge. Document management technology allows knowledge of an organization’s past, often dispersed among a variety of retention facilities, to be effectively stored and made accessible. Drawing on these
technologies, most consulting firms have created semantic memories by develop- ing vast repositories of knowledge about customers, projects, competition and the industries they serve.
Grover and Davenport (2001) found that by far the most common objective of knowledge management projects in Western organizations involves some sort of knowledge repository. The objective of this type of project is to capture knowledge for later and broader access by others within the same organization. Common re- pository technologies include Lotus Notes, web-based intranets and Microsoft’s Exchange, supplemented by search engines, document management tools and other tools that allow editing and access. The repositories typically contain a specific type of information to represent knowledge for a particular business function or process, such as:
• “Best practices” information within a quality or business process management function;
• Information for sales purposes involving products, markets and customers;
• Lessons learned in projects or product development efforts;
• Information around the implementation of information systems;
• Competitive intelligence for strategy and planning functions; and
• “Learning histories” or records of experience with a new corporate direction or approach.
The mechanical generation of databases, Web sites and systems that process data are good and have the potential to take us to a higher plane in the organization, help us understand workflows better and aid in dealing with organizational patholo- gies and problems. The data-to-information transition often involves a low level mechanical process that is well within the domain of contemporary information technologies, though humans are helpful in this transition as well. This information could exist in different forms throughout the organization and could even form the basis of competitive advantage or information products. For example, provision of information to customers about their order or shipment status is something that companies like Baxter and FedEx have been doing for years. But unlike knowledge, mechanically supplied information cannot be the source of sustained competitive advantage, particularly when the architectures on which it is based are becoming more open and omnipresent.
IT in knowledge management can be used to store various kinds of information.
For example, information about processes, procedures, forecasts, cases and pat- ents in the form of working documents, descriptions and reports can be stored in knowledge management systems. TietoEnator, a Scandinavian consulting firm,
has a knowledge base where they store methods, techniques, notes, concepts, best practices, presentations, components, references, guidelines, quality instructions, process descriptions, routines, strategies and CVs for all consultants in the firm (Halvorsen & Nguyen, 1999).
Knowledge retrieval can find support in content management and information extraction technology, which represent a group of techniques for managing and extracting knowledge from documents, ultimately delivering a semantic meaning for decision makers and learners alike. These types of computer applications are targeted at capturing and extracting the content of free-text documents. There are several tasks that fall within the scope of content management and information extraction (Wang et al., 2001):
• Abstracting.and.summarizing: This task aims at delivering shorter, informa- tive representations of larger (sets of) documents.
• Visualization: Documents can often be visualized according to the concepts and relationships that they play a role among. Visualization can be either in an introspective manner, or using some reference model/view of a specific topic.
• Comparison.and.search: This task finds semantically similar pieces of in- formation.
• Indexing.and.classification: This considers (partial) texts, usually according to certain categories.
• Translation: Context-driven translation of texts from one language into another.
Language translation has proven to be highly context specific, even among closely related languages. Some kind of semantic representation of meaning is needed in order to be able to make good translations.
• Question.formulation.and.query.answering: This is a task in human-com- puter interaction systems.
• Extraction.of.information: This refers to the generation of additional infor- mation that is not explicit in the original text. This information can be more or less elaborate.
A group of computational techniques are available to alleviate the burden of these tasks. They include fuzzy technology, neural networks and expert systems. On a more application-oriented level, there are several approaches that apply one or more of the general techniques. The field is currently dynamic, and new advances are made continuously. One novel approach is the CORPORUM system, presented in the section on expert systems.