An approach to aid understanding emerging research fields--the case of knowledge management.
New research fields emerge with people's new understanding of the world. Examples of new research fields emerging in the past 2 ~ 3 decades include complex adaptive systems, knowledge management (KM), and nanotechnology. Many people find themselves confused by such emerging fields. 'What is a complex adaptive system?', 'What is knowledge management?', 'What is nanotechnology?' are frequently asked questions. The main reason for the confusion might be that these new terms are umbrella concepts covering many factors, and the essential meanings of these terms are dynamic, thus there are no universal definitions of them. For example, searching for the exact phrase 'knowledge management' in Google, one of the most famous online search engines, gets around 48 thousand results. (1) As a more concrete example to show that people have different understandings of a new research field, Section 'Confusion Regarding an Emerging Field--an Example' will introduce a survey carried out in 2006 to assess how people understand a new research field--knowledge science.
One of the important knowledge bases for an emerging research field is peer-reviewed journals, which introduce and report work done regarding the research field. Reading papers in those journals can help people to learn about and understand specific research and practice details. However, most of the time, getting an overview of the emerging research field is still a remaining problem, since the research field might cover many factors, and practically it is infeasible for people to read all those papers and then summarize them. Of course, there are some summary articles or bibliographic works, written by experts in the research field. But as we mentioned above, different people have different understandings, and this also includes experts. Experts' explanations might be different from or even conflict with each other. For example, some KM experts believe that information technology is a very important part of KM (e.g. see Wiig, 1997); while others argue that KM is independent from information technology (e.g. see Bergeron, 2003).
This paper puts forward an approach to aiding people to get an overview of an emerging research field. Based on domain analysis, the approach focuses on how to organize keywords extracted from relevant journals in a way that people can easily get an overview of an emerging research field.
With the increasing body of text, and the open-access policies of many journals, literature mining has drawn increasing attention in recent years. Traditional literature mining commonly focuses on intelligent computing algorithms to perform text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization and entity relation modelling (e.g. see Feldman et al., 1998; Jensen et al., 2006). However, the approach introduced in this paper is not about computing algorithms, but a methodology for helping people to get an overview of an emerging research field.
Domain analysis is a method widely used in software engineering for identifying, collecting, organizing, and representing the relevant information in a domain (see Kang et al., 1990). In the tradition of software engineering, applications of domain analysis have been largely limited to describing something concrete. This paper explores a new type of application of domain analysis, by implementing it on a research field.
Our approach does not argue that people have to share the same understanding of a research field. By providing an overview of what has been often mentioned in the literature and applying an improved domain analysis, our approach aids people to explore answers to the following six fundamental issues regarding an emerging research field:
* Why the research field is necessary, or what its objectives are;
* What enables the birth of the research field, or triggers actions on it;
* What it deals with;
* How to implement it;
* How to support it;
* Where it has been applied.
From the viewpoint of system science, we can think an emerging research field as a system composed of a lot of emerging elements--denoted by keywords distributed in various publications. Our approach first collects those elements from relevant publications, then evaluates those elements--by calculating their frequency of occurrence and co-occurrence, and then develop a hierarchy structure of those elements corresponding to the six fundamental issues. As a structured system is much easier for understanding than an ill-structured one, the hierarchy structure of keywords can aid people to easily get an overview of an emerging research field.
Although the approach is inspired by domain analysis--a methodology widely used in IT (information technology), it is not a complete 'hard' approach. Instead, the 'soft' system thinking plays a more important role in it, i.e. the approach involves many subjective expertise inputs. For example, when some leading publications do not provide keywords, expertise is required for identifying keywords, and when developing the hierarchy of keywords, subjective expertise is also inevitable. (2) Further, with the hierarchical structure, people can still get their own different overview or understanding of an emerging research field. Our approach is like preparing elements for cooking and put them in the right place, but dishes cooked by different chefs with the same elements, even with the same recipe, could be of different tastes. Of course, elements provided by different people could be different. It is most likely that cooking materials prepared by Japanese are different from those prepared by Mongolia. That is to say, the result of applying the approach is inevitable to depend on who are involved in the process.
A case study of using this proposed approach was conducted to aid people to explore the six fundamental issues of 'Knowledge Management'. As we are entering a knowledge economy and knowledge society (see OECD, 1996), the ability to manage knowledge has become increasingly important for an organization to survive and maintain its competitive advantage (see Nonaka and Takeuchi, 1995; Shariq, 1997; Li and Zhao, 2006; Qi et al., 2006). With this changing situation, KM was born as an emerging research field; meanwhile, many new terminologies within KM were invented, such as, chief knowledge officer (CKO), knowledge co-ordinator, knowledge creator, knowledge facilitator, etc. (Guns, 1997; Ellinger et al., 1999). KM seems to cover almost everything, and has been given many different definitions (e.g. see Kanter, 1999), without one becoming dominant. Therefore, it is not surprising that people are confused by KM, and our case study aims to provide support for getting an overview of KM.
The remainder of this paper is organized as follows: Section 'Confusion Regarding an Emerging Field--an Example' introduces a survey to show a concrete example of how people have different understandings of an emerging research field. Section 'An Approach Based on Domain Analysis to Aid Understanding Emerging Research Fields' introduces our approach for aiding people to get an overview of an emerging research field. Section 'A Case Study: Application of the Proposed Approach to Knowledge Management' gives a case study of applying the approach to explore the six fundamental issues regarding KM. Section 'Limitations and Application Boundaries of the Approach' discusses limitations and application boundaries of the approach. Section 'Concluding Remarks' summarizes this paper and gives concluding remarks.
CONFUSION REGARDING AN EMERGING FIELD--AN EXAMPLE
In 1998, Japan Advanced Institute of Science and Technology (JAIST) established a school called School of Knowledge Science, which is believed to be the world's first research and education institute established under the theme of knowledge. After 8 years of research and practice within this research field, students and faculty in the school found themselves still being asked frequently 'What is Knowledge Science', and most of them found it was difficult to answer this question. In 2006, a survey was conducted in the School of Knowledge Science to obtain a working definition of Knowledge Science (KS). The researchers with positions as 'post-doctoral researchers' or holding higher positions were invited to take part in the survey. There were in total 20 respondents, with four professors, seven associate professors and nine assistant professors and post-doctoral researchers. Among these 20, some of their answers are listed below:
(1) KS is a study of creativity ...
(2) KS is a systematic study of knowledge ...
(3) KS is a study of human science ...
(4) KS is a study of efficient methods of knowledge transfer, knowledge utilization and knowledge creation ...
Given the size of this survey (including only 20 respondents), we were able to ask a group of experts to perform a subjective measurement of similarity and dissimilarity among the answers. As shown in Figure 1, a classification of 10 groups was finally achieved. An associated key to this figure is shown in Table 1, interpreting what each group in Figure 1 stands for. The results show that, among 20 respondents, six of them (A group) argued that KS is about creativity, knowledge creation and knowledge use; five of them (B group) argued that KS is about human science and social science; and for the rest, for instance, one of them (AC group) argued that KS is a combination of A group and C group, that is, KS is about creativity, knowledge creation and knowledge technologies, etc.
[FIGURE 1 OMITTED]
This survey gives us a concrete example that people in a small research group (in our case, School of Knowledge Science), have very different understandings of an emerging research field. Considering the fact that a lot of new research fields have emerged from several existing fields (that is why they are commonly called interdisciplinary research fields), and commonly the terms used to describe them are umbrella concepts covering many factors, it is natural that people may have different understandings and definitions of them. This paper does not argue that people must share the same understanding of a research field, but aims to help those who are confused about a research field by providing them an overview of what has been often mentioned in the literature regarding the research field. The approach put forward in the next section has been developed for this purpose.
AN APPROACH BASED ON DOMAIN ANALYSIS TO AID UNDERSTANDING EMERGING RESEARCH FIELDS
A good understanding of a research field mostly is a personal issue that requires individual learning, thinking and experience. However, the process of understanding a research field can be partly supported by, for example, discussions with expertise from the same field because people can learn about a new research field quickly by asking the opinions of experts in the field. Apart from non-technical methods, this process also can be helped by some technical methods: data mining, data representation, statistical analysis, visualization, and the like, for example, a useful technique of mining the best cited articles about a research topic from the online electronic databases. Inspired by domain analysis, our approach tries to collect and organize data and information in a way that people can get an overview of an emerging research field easily.
Domain analysis is 'the process of identifying, collecting, organizing and representing the relevant information in a domain, based upon the study of existing systems and their development histories, knowledge captured from domain experts, underlying theory and emerging technology within a domain' (Kang et al., 1990). It aims at identification of an issue and enhancing understanding of the domain by capturing the information in either informal or formal modelling descriptions.
The idea of domain analysis originally came from software engineering for building reusable components, first introduced by Neighbors in 1981. Then Prieto-Diaz (1987), and Arango (1989) proposed a more cohesive procedural SADT (structured analysis and design technique) model for performing domain analysis. Later, Bjorner (2006) developed a complete theory of domain engineering in his three textbooks on engineering principles and techniques of software engineering.
Domain analysis is different from systems analysis; systems analysis is concerned with the objects and operations in a specific system, while domain analysis is concerned with objects and actions in a class of similar systems in a particular problem domain (Neighbors, 1981). In the triptych dogma of software engineering interpreted below, systems analysis can be associated with understanding requirements, while domain analysis can be associated with understanding domain (Bjorner, 2006).
'Before software can be designed, programmed or coded, its requirements must first be reasonably well understood.'
'Before requirements can be expressed properly, the domain of the application must first be reasonably well understood.'
Domain analysis embodies many development principles, techniques and tools. However, as Bjorner (2006) suggested in his book, to pragmatically identify and describe a domain, we first need to know four basic categories/metaconcepts in terms of entities, functions, events and behaviours. He also mentioned support technologies as a way of analysing a domain. We explain these five concepts in Table 2, with an example.
The Proposed Approach
An emerging research field such as Knowledge Management can be understood as a domain which covers a lot of factors. Different definitions and explanations of an emerging research field might result in wide confusion about what it is. Implementing domain analysis to explore Entities, Functions, Events, Behaviours and Support Technologies of an emerging research field can aid understanding of it. For an emerging research field, Entities in domain analysis answers the question 'What does it deal with?'; Events answers the question 'What enables its birth, or triggers actions on its entities?'; Functions and Behaviours answer the question 'How to implement it?'; and Support Technologies answers the question 'How to support it?'. Besides the five aspects in the tradition of domain analysis, we argue that identifying two additional aspects, objectives and applications, will give a more complete story of an emerging research field. Objectives answers the question 'Why is it necessary?', and applications answers the question 'Where has it been applied?'. Table 3 summarizes how the five traditional aspects and the additional two are mapped onto six important issues regarding an emerging research field.
Inspired by Prieto-Diaz's SADT procedural model (see Prieto-Diaz, 1987) for domain analysis, we propose a diagram, Figure 2, to describe the process of implementing domain analysis on an emerging field. As shown in Figure 2, the main domain knowledge input (for implementing domain analysis on an emerging field) is from scientific literatures. Of course the selection of literature is somehow depends on subjective expertise. If practicable within financial constraints, question surveys and expert advice are additional inputs. In this diagram, we apply a structure composed of five phases to analyse six fundamental issues regarding an emerging research field, as mentioned in Table 3. Domain analysts mean those who specialize in implementing domain analysis. Domain analysts here can be understood as knowledge coordinators (see Nakamori, 2003; Ma et al., 2006). They are expected to have some knowledge about the domain (the emerging research field). Domain experts work together with domain analysts in certain steps of domain analysis where specialized knowledge about the emerging research field is required. Output of the domain analysis is a hierarchy structure of keywords, which can aid understanding of the field.
Our approach of implementing domain analysis on a research field is composed of the following five phases.
Phase 1. Select leading journals regarding the research field. Surveying relevant leading researchers and practitioners could be a good way to collect their knowledge about an emerging research field. However, such kind of work could be very time and resource consuming. In our approach, we suggest the more practical way to be select and collect necessary information from leading journals regarding the research field. Some agencies provide impact factors of scientific journals, and such information is useful for selecting leading journals regarding the research field.
[FIGURE 2 OMITTED]
Phase 2. Extract keywords from publications in the leading journals. Most leading journals require the author(s) to provide keywords of the submitted paper, based on the fact that keywords roughly tell what the paper is about, and having the keywords in mind can quickly help the readers find summarized research topics of the paper. In this phase, we extract keywords of all publications from selected journals and store them in a database. Of course, sometimes the information provided by only keywords themselves is not sufficient, and it is highly intuitive to understand what these keywords mean directly. It is necessary to refer back often to the articles where these keywords appeared to understand what is really meant by them. It is possible that some leading journals do not provide keywords. For not loosing the input from those journals, it is necessary to identify keywords from titles or abstracts manually. Identifying keywords from full contexts will be very time consuming.
Phase 3. Select and analyse extracted keywords statistics and visualization. The number of keywords might be huge. Some of them might have little relation with the essentials of the research field. In this phase, statistics is used to select the most important keywords measured by their frequency, and visualization is used to visually display the relations among the keywords, which provides a more direct and easier way to understand data, and to identify hidden complex patterns behind the data and relationships.
Phase 4. Map keywords to the seven aspects regarding the research field. This phase explores the seven aspects of the research field, which are entities, events, functions, behaviours, support technologies, objectives/targets and applications. In this phase, carefully selecting the right experts is strategically important. These invited experts work together with domain analysts to discuss the meaning of each selected keyword from Phase 2 and Phase 3, and also the seven aspects in the context of the research field, and then matched the selected keywords and the seven aspects. This process should be repeated several times until there are no keywords which still need to be assigned.
Phase 5. A hierarchy structure of keywords for aiding understanding the research field. The result of assigning keywords to the seven aspects generates a hierarchy structure that assists in understanding the research field. The hierarchy structure could be interpreted to find answers to the six issues regarding the research field: What does it deal with, what enables its birth or triggers actions on its entities, how to implement it, how to technologically support it, why is it necessary, where has it been applied.
So far, the applications of domain analysis have been limited to describing something concrete. Examples of domains range from buildings, air traffic, transportation and the financial service industry to markets, and so on. Therefore, describing a research field, which is conceptual, is along a different line, and is a new type of application. In the next section, we will introduce how to implement the approach introduced in this diagram regarding the Knowledge Management field.
A CASE STUDY: APPLICATION OF THE PROPOSED APPROACH TO KNOWLEDGE MANAGEMENT
As an interdisciplinary research field which has emerged recently, KM has been given many different definitions (e.g. see Kanter, 1999), without one dominating others. KM seems to cover almost everything, in other words, it is difficult to understand what the essentials of KM are.
Since the beginning of the 21st century, several summarized studies have been carried out with the aim of providing a general understanding of KM. Sugiyama et al. (2002) introduced and elaborated 64 most important keywords in the field of Knowledge Science, a concept related to KM, such as knowledge creating company, SECI model, Ba and tacit knowledge. Then Serenko and Bontis (2005) conducted a meta-review of KM by investigating three leading peer-reviewed journals in this area, namely, Journal of Intellectual Capital, Journal of Knowledge Management and Knowledge and Process Management, in which research productivity and citation analysis were applied to rank researchers, institutions, countries and publications regarding KM at the worldwide level, for example, leading authors such as Nonaka, I., and Davenport, T.H., and key publications such as The Knowledge Creating Company and Working Knowledge were referenced regularly. Saito (2007) summarized the KM field in terms of four basic epistemological perspectives, with each leading to different ways of understanding knowledge and its management: information, human, computing and strategy. Those previous studies summarized important keywords of knowledge science, revealed leading authors and distinguished research papers in the KM field, and provided basic epistemological perspectives of KM. Yet an overview of KM content at the world-wide level is a remaining problem. The purpose of our case study is to aid people to get an overview of KM by applying domain analysis on it.
This subsection corresponds to Phase 1 and Phase 2 of our approach, which we discussed in the previous section: selecting leading journals of the KM field, and extracting keyword information from those journals.
With the increasing importance of KM, a large number of newly established KM specific journals and special issues on KM from existing journals have been booming up. We carefully designed a set of strategies to select the most influential journals and special issues out of vast databases: (a) all the articles in the journal and special issues should be focused on KM; (b) In the articles, the keywords must be specified by author(s). However, this constraint was relaxed for some very prominent publications, for example, Harvard Business Review on Knowledge Management and California Management Review: special issue on Knowledge and the Firm, although the papers inside it did not provide keywords, we think those articles are really milestones in KM, so we identified several keywords from them manually; (c) The journals should be well-recognized, which can be measured by knowing if the journals have a fairly long publishing history or have a peer-review process, etc.
Eventually, four journals and 17 special issues covering both soft perspectives and hard perspectives on KM were chosen. Out of all keywords extracted from them, we collected a total of 300 most important keywords ranked by their frequency, since we found that not all keywords from data sources are essential to KM. As a matter of fact, some keywords appeared several times, because the 300 keywords were extracted from different journals and there was a chance of overlap. Table 4 provides more details of this scientific literature and the number of keywords selected from them.
Basic Data Analysis
This sub-section corresponds to Phase 3 of our approach. Before assigning the selected keywords to the seven aspects, we did some analysis of them using statistics and visualization. Taking Journal of Knowledge Management as an example, basic data analysis investigated frequency of keywords, relations between keywords, and visualization of keyword relations, etc. Table 5 tells us the most frequent keywords are knowledge management, innovation, intellectual capital, learning organizations, etc. Table 6 relates one keyword with another one in terms of their co-occurrence (that is, they appeared together in the keyword lists of one or more articles specified by authors). Figure 3 shows the relations denoted in Table 6, which provide a more direct and easier way to understand data, and help identify hidden complex patterns behind the data and relationships. In Figure 3, it is easy to see that two isolated groups are formed, and the smaller one includes only two keywords: management and information; while in the bigger group, knowledge management lies in the center and acts as broker/bridge between many other pairs of keywords.
In the previous two sub-sections, we collected 300 most important keywords of the KM field and performed basic data analysis among these keywords. This sub-section corresponds to Phase 4 of our approach to respond to the seven aspects of KM.
Practically, we invited several KM domain experts, and held a series of meetings with them. We discussed the domain analysis methodology and how to map the keywords to the seven aspects. Our discussions suggested that the seven aspects could be further divided into their sub-aspects. As a result, the aspects of entities and functions were each further divided into five sub-aspects, namely, general, strategy-oriented, information-oriented, human-oriented and process-oriented; the aspect of events was further divided into two sub-aspects, namely, external and internal; and the aspect of support technologies was further divided into two sub-aspects, namely soft and hard. We explain these sub-aspects below:
* General KM: KM focuses on general management issues within an organization.
* Strategy-oriented KM: KM focuses on building organizational capability to fulfill the organization's mission.
* Information-oriented KM: KM focuses on managing the codified explicit content within an organization that can be easily stored or transferred.
* Human-oriented KM: KM focuses on managing the intangible human capital within an organization that is difficult to articulate or transfer.
* Process-oriented KM: KM focuses on managing the core business process or innovation process within an organization.
* External events: refer to the external changes that trigger KM practices within an organization
* Internal events: refer to the internal changes that trigger KM practices within an organization
* Soft support technologies: are the non-technological efforts or managerial efforts that are used to facilitate KM practices within an organization.
* Hard support technologies: are the technological efforts or information systems that are used to facilitate KM practices within an organization.
The result of classification of the 300 keywords into the seven aspects and their sub-aspects is reported in Table 7.
Table 7 can be seen as a structured hierarchical taxonomy for aiding understanding of KM (Phase 5). With the taxonomy, different people can give different interpretation based on individual knowledge and taste. The following is our interpretation.
Resulting from restructuring changes both outside and inside an organization, on the outside, such as, economic growth, globalization, knowledge society, on the inside, such as learning organization, culture change and community of practice, KM has been established to improve organizational competitive advantage, organizational competences, etc. KM is dedicated to deal with strategy-oriented knowledge (organizational culture, corporate strategy, etc), information-oriented knowledge (information, explicit knowledge, etc.), human-oriented knowledge (intellectual capital, intangible assets, etc.) and process-oriented knowledge (knowledge process, etc.). Soft methods such as knowledge workers, CKOs, and hard technologies such as information technology, information systems, KM systems, have been developing to support KM. So far, KM has been applied to project management, product development and many other areas. KM has been criticized as one more means to exploit labour and reinforce unfair social relations.
We presented the approach and the case study at several international conferences and especially in the International Society for Knowledge and System Science, and also introduced them to some researchers and graduate students in our school who were confused by KM. The feedback indicates that our study was helpful for getting an overview of KM.
[FIGURE 3 OMITTED]
LIMITATIONS AND APPLICATION BOUNDARIES OF THE APPROACH
As we mentioned above, the application of the approach will inevitably involve subjective expertise such as during selecting scientific literature and deciding the hierarchical structure of the taxonomy. Thus the resulted taxonomy itself could be a subjective, ideological and even political product. Or we can say, our approach is a tool, what it can produce depends on who, how, for what purpose and under what conditions to use it. And further more, the approach could be a non-neutral tool, in the sense that it directs an emerging research field in particular directions chosen by the users.
For example, in our case study, almost all our experts had a positive feeling about KM, with the belief that KM has become increasingly important for an organization to survive and maintain its competitive advantage. This is not surprising since most people are positive on the field they are working in. Otherwise they will not work in the field and thus not be experts in the field. With such selection of experts, it could be that some important opinions from other perspectives were excluded unconsciously. For example, from neo-Marxist, critical and postmodernist perspectives, KM is not necessarily a good thing; instead it is one more, fashionable and clever, means for capital to exploit labour, to reinforce unfair, unequal, injustice social relations. The overview our case study provided is what have been largely mentioned in the literature known by us and our experts. It could be that some other important literature (maybe they were not presented under KM topic but gave important, especially critical, arguments on KM) were not included as resources. Readers should be careful of this point.
Another point we would like to remind readers to take caution is: as an emerging research field, its scope, contents, and interlinks among its elements are dynamic. And keeping in mind that the result of the approach is somehow expert-dependent, we cannot and should not use the result of our approach to close down debates and impose a specific order in an emerging field. Actually it would be interesting to explore the dynamics of an emerging research field by applying our approach to analyse literature at different time period. Of course this would again somehow rely on subjective expertise.
This paper puts forward an approach based on domain analysis to help people to get an overview of a research field about which they are confused. The approach extends the traditional domain analysis methods by adding two additional aspects: objectives and applications regarding a research field. Treating a research field as a conceptual domain, the approach introduced in this paper explores a different line, and is a new type of application of domain analysis, while traditional applications of domain analysis have been limited to describing something concrete.
Our approach consists of five phases, from data collection, extracting necessary information, performing basic data analysis, implementing domain analysis, to interpreting results. Our approach aims to help people to explore six fundamental issues regarding a research field. These six issues are: why the research field is necessary, what enables its birth or triggers actions on it, what it deals with, how to implement it, how to support it and where it has been applied.
We applied this approach to a new emerging interdisciplinary research field: KM. Importantly, our findings also suggest that KM could be divided into general, strategy-oriented, information-oriented, human-oriented, and process-oriented perspectives. Here we remind readers again that the result, especially the interpretation of the result, was generated with some subjective expertise.
Mining the literature is a part of our approach. Because the result of mining the literature is based on work already done, and the essentials of a research field are dynamic, the literature mining approach needs to be repeated after a period of time to support people to get an up-to-date overview of a research field. A computer support system will be very helpful for the application of the approach, especially when the literature is available from well-structured online databases.
Finally, we would mention again that our approach itself does not provide understanding of emerging research fields, instead its main objective and function is to aid and help people to understand emerging research fields.
Published online 23 March 2009 in Wiley InterScience (www.interscience.wiley.com) DOI 10.1002/sres.926
This research is supported by the 21st COE (centre of excellence) Program 'Technology creation based on knowledge science: theory and practice' of JAIST, a fund by Ministry of Education, Culture, Sports, Science and Technology, Japan.
Received 20 August 2007
Accepted 26 August 2008
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(1) We did this search on July 21, 2007. The phase knowledge management was put in quotation marks when performing this search; without the quotation marks, search engine returned 462 million results.
(2) The hierarchy of keywords can be looked at as simple ontology of the research field, and it is widely realized that no good ontology can be developed without subjective input.
Kun Nie  *, Tieju Ma  and Yoshiteru Nakamori 
 College of Business Administration, Zhejiang Gongshang University, No.18, Xuezheng Street, Xiasha University Town, Hangzhou, 310018 China
 School of Business, East China University of Science and Technology, Meilong Road 130, Shanghai, 200237, China
 Graduate School of Knowledge Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
* Correspondence to: Kun Nie, College of Business Administration, Zhejiang Gongshang University, No. 18, Xuezheng Street, Xiasha University Town, Hangzhou, 310018, China.
E-mail: email@example.com [Correction made here after initial publication]
([dagger]) This article was published online on [23 March 2009]. An error was subsequently identified. This notice is included in the online and print versions to indicate that both have been corrected [20 July 2009].
Table 1. The Key to the graph Numbers of Groups Content of each group responses A Creativity, knowledge creation, knowledge use 6 B Human science, social science 5 C Knowledge technologies, knowledge systems 0 D Knowledge process 2 E Management of Information 1 F Knowledge itself 2 G Solve problems produced by knowledge society 1 AC 1 CD 1 ABCD 1 In total: 20 Table 2. Introduction to entities, functions, events, behaviours, and support technologies Aspects * Definition in software A brief incomplete engineering" example of 'banking' Entities Something fixed, immobile [Demand/deposit, savings, or static, if implemented mortgage] bank account; inside computers, could money; clients; bankbook, typically be represented etc. as data. Events The occurrence of Losing a bankbook, etc. something that may either trigger an action, or is triggered by an action, or alter the course of a behaviour, or a combination of these. Functions A mathematical quantity, [Open, close, deposit, which when applied to withdraw, transfer] entities, either tests operations on accounts, for some property, or etc. observes some sub-entity, or actually changes the entity value Behaviours A sequence of actions and A specific series of events deposit and withdrawal events and actions, etc Support Ways and means of ATM machine; bankcard, technologies implementing certain etc. observed phenomena or concepts * Adapted from Bjorner, (2006). Table 3. Seven aspects and their six corresponding issues regarding an emerging research field Six corresponding issues Short Seven aspects in an emerging field summary Five aspects Entities What does it deal with Know-what in traditional domain analysis Events What enables its Know-where birth, or triggers actions on its entities Functions How to implement it Know-how Behaviours Know-how Support How to support it Know-how technology Two additional Objectives Why is it necessary Know-why aspects Applications Where has it been Know-where applied Table 4. Data input Data resources Data description Soft perspectives on KM: Journal of Knowledge Management is Journal of Knowledge Management a peer-reviewed KM publication since 1997, 100 most frequent keywords are collected and selected from the total 411 articles in this journal. Knowledge Management Research Knowledge Management Research & & Practice Practice is a peer-reviewed KM publication since 2003, 50 most frequent keywords are collected and selected from the total 101 articles in this journal. Hard perspectives on KM: Journal of Knowledge-based System Journal of Knowledge-based is a peer-reviewed KM publication Systems since 1987, 50 most frequent keywords are collected and selected from the total 720 articles in this journal. Mixed perspectives on KM: International Journal of Knowledge International Journal of and Systems Sciences is a Knowledge and Systems Sciences peer-reviewed KM publication since 2004. 50 most frequent keywords are collected and selected from the total 107 articles in this journal. 17 special Issues on Knowledge 17 special issues are chosen from Management 11 different journals, 50 most * Harvard Business Review on frequent keywords are collected Knowledge Management, 1998 and selected from the total 120 * California Management Review, articles from these 17 special 40(3), 1998. issues. (Special issue: Knowledge and the Firm) * Organization Studies, 24(6), 2003. (Special issue: Knowledge and Professional Organizations) * Human Relations, 54(7), 2001. (Special issue: Knowledge Management in Professional Service Firms) * Management Science, 49(4), 2003. (Special Issue on Managing Knowledge in Organizations: Creating, Retaining, and Transferring Knowledge) * Decision Sciences, 34(2), 2003. (Special issue: Knowledge Management) * International Journal of Accounting Information Systems, 3(2), 2002. (Knowledge Management Issues in Practice: Opportunities for Research) * Decision Support Systems, 39(4), 2005. (Special issue: Collaborative Work and Knowledge Management) * Decision Support Systems, 36(4), 2004. (Special issue: Knowledge Management Technique) * Decision Support Systems, 31(1), 2001. (Special issue: Knowledge management support of decision making) * Information Systems Frontiers, 2(3-4), 2000. (Special issue: Knowledge Management and Organization Memory) * Information Visualization, 5(3), 2006. (Special Issue: Integrating knowledge and information: digital concept maps as a bridging technology) * Journal of Management Information Systems, 18(1), 2001. (Special Issue: Knowledge Management) * Journal of the Operational Research Society, 54(9), 2003. (Part Special issue: Modeling Organizational Knowledge) * Journal of the Operational Research Society, 54(2), 2003. (Special Issue: Knowledge Management) * MIS Quaterly, 29(1), 2005. (Special Issue on Information Technologies and Knowledge Management) * MIS Quaterly, 29(2), 2005. (Special Issue on Information Technologies and Knowledge Management) Table 5. The most frequent keywords from Journal of Knowledge Management Keywords Frequency Knowledge management 291 Innovation 38 Intellectual capital 28 Learning organizations 21 Information 18 Knowledge workers 18 Learning 17 Tacit knowledge 17 Management 16 Information technology 15 Organizational learning 15 Knowledge processes 14 Organizations 14 Competitive advantage 14 Knowledge creation 12 Information systems 11 Knowledge 11 Networks 11 Knowledge transfer 10 Knowledge management systems 9 Table 6. Relations between keywords Number of co- Keyword 1 Keyword 2 occurrences * Knowledge management Innovation 28 Knowledge management Intellectual capital 17 Knowledge management Knowledge processes 13 Knowledge management Organizations 13 Information Management 12 Knowledge management Competitive advantage 11 Knowledge management Learning organizations 11 Knowledge management Information technology 11 Knowledge management Organizational learning 10 Knowledge management Tacit knowledge 10 Knowledge management Information systems 9 Explicit knowledge Tacit knowledge 8 Knowledge management Project management 7 Knowledge management Learning 7 Knowledge management Knowledge management systems 7 Knowledge management Knowledge transfer 7 * only relations with more than 6 co-occurrences are denoted here, and also in Figure 3. Table 7. Classification of the 300 keywords into the seven aspects and their sub-aspects Aspects Sub-aspects Typical research topics Entities General Organization, knowledge, resources, knowledge-based organization, knowledge base Strategy-oriented Organizational culture, corporate culture, organizational climate, corporate strategy, business strategy, leadership, strategic knowledge Information-oriented Information, explicit knowledge Human-oriented Intellectual capital, intangible assets, intellectual property, human capital, intellectual assets, social capital, teams Process-oriented Knowledge process economic Events External growth, globalization, knowledge economy, knowledge society, knowledge market Internal Learning organization, culture change, business process reengineering, community of practice, virtual organization, boundary spanning Functions General Resource management Strategy-oriented Strategy management, organizational design, management strategy Information-oriented Management of information, information exchange, information networks, information transfer, knowledge mapping, information management, information visualization, knowledge discovery, knowledge capture, knowledge navigation, knowledge retrieval, knowledge extraction, knowledge repres- entation, semantics, case based reasoning, data mining, machine learning, knowledge acquisition, knowledge visualization, codification, concept map Human-oriented Networks, human resource management, cognition, training, narratives, collaboration, team work, language, sense making, communication, motivation, social networks, trust, discussions, team learning Process-oriented Process management Behaviours (No sub-aspect) Innovation, learning, organizational learning, Internet-resourced learning, knowledge creation, knowledge transfer, knowledge sharing, decision making, creativity, performance measurement, benchmarking, modelling, knowledge sharing, problem solving, implementation, best practices, integration, action learning, knowledge engineering, Ba Support Soft Knowledge workers, chief Technologies knowledge officer Hard Information technology, information systems, KM systems, intranets, computer applications, groupware, expert systems, decision support systems, rule-based systems, human-computer interaction, knowledge systems, collaborative systems Objectives (No sub-aspect) Competitive advantage, performance, organizational performance, competences, organizational development, exploit labour, reinforce unfair Applications (No sub-aspect) Cities, project management, product development, banking, aerospace industry
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|Title Annotation:||Research Paper|
|Author:||Nie, Kun; Ma, Tieju; Nakamori, Yoshiteru|
|Publication:||Systems Research and Behavioral Science|
|Date:||Nov 1, 2009|
|Next Article:||How entrepreneurial orientation moderates the effects of knowledge management on innovation.|