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Purpose – The main purpose of this paper is to conduct an in-depth theoretical review and analysis for the ﬁelds of knowledge management (KM) and investigate the future research trend about KM. Design/methodology/approach – At ﬁrst, few theoretical basis about KM which include deﬁnitions and stages about KM have been summarized and analyzed. Then a comprehensive review about the major approaches for designing the KM system from different perspectives including knowledge representation and organization, knowledge sharing and performance measure for KM has been conducted. Findings – The contributions of this paper will be useful for both academics and practitioners for the study of KM. Originality/value – For this research, the focus is on conducting an in-depth theoretical review and analysis of KM. Keywords Knowledge management, Literature review, Design approaches Paper type Literature review 1. Introduction In recent years, knowledge has been widely recognized the most crucial competitive asset (Palacios and Garrigos, 2006). Knowledge refers to a theoretical or practical understanding of a subject. Knowledge management (KM) has become a very common term in the twenty- ﬁrst century, as it has been applied to a wide spectrum of activities and areas with the purpose of managing, creating and enhancing intellectual assets (Shannak, 2009). And it has become enriched with a huge wealth of contributions from many scholars and an extensive accumulation of experiences. From a deeper point of view, KM should be a kind of working method and philosophy. KM is a part of the ﬁeld of management studies, but it is also closely integrated with information and communication technologies (Mihalca et al.,2008). In fact, KM can be observed from several perspectives, as there are a number of ﬁelds that contribute to it. Prominent among them are the ﬁelds of philosophy, cognitive science, social science, management science, information science, knowledge engineering, artiﬁcial intelligence and economics (Kakabadse et al., 2003). © Tingwei Gao, Yueting Chai and Yi Liu. Published in the International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons International Journal of Crowd Science Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to pp. 42-51 Emerald Publishing Limited full attribution to the original publication and authors. The full terms of this licence may be seen 2398-7294 DOI 10.1108/IJCS-08-2017-0023 at http://creativecommons.org/licences/by/4.0/legalcode Why the need to manage knowledge? Nowadays we are in the era of knowledge. The Theoretical reason of increased importance of knowledge lies in the fact that effective management of conception knowledge brings many positive outcomes to improve learning efﬁciency. And we implement KM initiatives with the expectation that it will result in increased competitive advantage. KM is used to capture, document, retrieve and reuse knowledge, as well as to create, transfer and exchange it (Dayan and Evans, 2006). There is no limit to where KM can be applied, ranging from individual learning, small enterprises to large multinational corporations: KM has become increasingly more important for individuals to understand what information is essential, how to administer this essential information and how to transform essential information into permanent knowledge (Tseng et al., 2012); KM plays a fundamental role in the success of an organization’s activities and strategies (Castrogiovanni et al.,2016). Therefore managing and using knowledge effectively is vital for both individuals and organizations to take full advantage of the value of knowledge. During the past decade, numerous publications dealing with KM reviews from different perspectives have been published. Ragab and Arisha (2013) categorized different branches of KM research. Serenko (2013) analyzed the stock of KM publications and identiﬁed citation classics in KM ﬁeld. Makhsousi et al. (2013) reviewed recent advances on the implementation of KM in different areas and discussed why some of KM implementations fail and how they could turn into a successful one. Arisha and Ragab (2013) provided a literature review and categorized the analysis of the rapidly growing number of KM publications, and they offered a comprehensive reference for newcomers embarking on research in the ﬁeld. Matayong and Mahmood (2013) reviewed the current literature of KM systems studies in organizations. Chiliban et al. (2014) reviewed different KM models based on their strengths and weaknesses. Tzortzaki and Mihiotis (2014) studied how the theory revolving around KM has developed over the years. Omotayo (2015) reviewed the literature in the area of KM to bring out the importance of KM in an organization. Asrar-ul-Haq and Anwar (2016) reviewed the attempts to provide the evidence base concerning knowledge sharing and KM in organizational settings. Based on the above-described scenario, in this research, we aim to provide a systemic overview of KM. And we accomplish this task by a series analysis approaches, such as literature bibliometric, theoretical basic analysis and designing approaches’ re-view. At last, our main contributions can be related to the Streams (A) and (B) as follows: (A) we summarize and analyze some major theoretical conceptions about KM and (B) we give a comprehensive review about the approaches for designing the KM system. The remainder of this paper is organized as follows. In Section 2, we review the major conception of KM. Section 3 shows and analyzes the approaches to design KM system. Finally, conclusions are presented in Section 4. 2. Theoretical conception of knowledge management 2.1 Deﬁnition of knowledge management There are a number of approaches to the conception about knowledge, as it is both a complex and abstract term. Actually, the deﬁnition of knowledge is a matter of ongoing debate among philosophers in the ﬁeld of epistemology. One of the most accepted deﬁnitions about knowledge is that knowledge is a dynamic human resource of justiﬁcation of the personal beliefs to obtain the truth (Nonaka, 1994). It can then be stated that knowledge is an invisible or intangible asset, in which its acquisition involves complex cognitive processes of perception, learning, communication, association and reasoning (Epetimehin and Ekundayo, 2011). Knowledge is the concept, skill, experience and vision that provides a framework for creating, evaluating and using the information (Soltani and Navimipour, 2016). Generally, knowledge can be divided into two types, tacit and explicit (Hubert, 1996). Tacit knowledge IJCS is the personal and context-speciﬁc knowledge of a person that resides in the human mind, 2,1 behavior and perception (Duffy, 2000). Koenig (2012) suggested that explicit knowledge means information or knowledge that is set out in tangible form. Also there are many deﬁnitions and descriptions about KM written by different scholars from various ﬁelds. These deﬁnitions are somewhat unclear and have different meanings depending on the authors’ views. To have a deep understanding of KM, we should re-visit some fundamentals of KM, such as the theoretical understanding of the concept of knowledge despite the abundance of theoretical and conceptual work. We have reviewed some major conceptions of KM and summarized them in Table I. When reviewing the deﬁnitions about KM, there are some terms that seem more central and fundamental than others, such as organization and information. In summary, despite the various versions of the deﬁnition and descriptions about KM, their essence is to help individuals improve learning efﬁciency and integrate different information resources to improve competitiveness advantages. And KM is capable of providing the individual with the tools and techniques they need to surmount the overwhelming information they encounter and to enable them to improve learning efﬁcacy and increase competitive advantage. 2.2 Process and stages of knowledge management KM is viewed as a process, where many related activities are formed to carry out key elements of strategy and operations for KM. During the past two decades, a vast number of KM processes have been introduced by researchers from different perspectives. And we reviewed and summarized some major descriptions about KM process. Table II shows this result. Although there are various descriptions about KM process, some words seem more central and fundamental than others, such as creation, storage, transfer and application. Knowledge creation refers to how new knowledge is created. This stage involves the developing of new content or the replacing of existing content within the tacit and explicit knowledge (Ajmal and Koskinen, 2008). Knowledge storage refers to the process of recording knowledge and storing it in the repositories such as archives, databases and ﬁling systems. And it aims to transfer the knowledge to the individual, groups or units that need to apply it (Johannsen, 2000). Knowledge transfer is an important process of KM and refers to the transfer of knowledge to locations where it is needed and can be used (Pirkkalainen and Pawlowski, 2013). This phase is critical for the success of the KM process, as the transfer must produce changes in the knowledge base (Argote and Ingram, 2000). Knowledge application refers to the actualizing of knowledge. This process can be used to adjust strategic direction, solve new problems, improve efﬁciency and reduce costs (Newell et al.,2004). And this stage is used to make good use of the created knowledge such as implementing a best practice. 3. Designing approaches for knowledge management 3.1 Knowledge representation and organization Knowledge representation and organization is a technique that increasing efﬁciency of an explaining associations of knowledge bodies with the purpose of managing knowledge by creating similar content associations. During the past decade, the semantic link network (SLN) has been widely used in the ﬁeld of KM. SLN is a network that represents semantic relations between concepts. And it is always used as a form of knowledge representation. It consists of vertices, which represent concepts, and edges, which represent semantic relations between concepts (Hai, 2011). Theoretical Authors Year Description conception Horwitch & 2002 The creation, extraction, transformation and storage of the correct Armacost knowledge and information in order to design better policy, modify action and deliver results Skyrme 2003 The explicit and systematic management of vital knowledge and its associated processes of creating, gathering, organizing, diffusion, use and exploitation April & Izadi 2004 The philosophy of knowledge management is made up of both the collect function (data and information dimensions) and the connect function (knowledge and wisdom function) Pearce-Moses 2005 The administration and oversight of an organization’s intellectual capital by managing information and its use in order to maximize its value Wang 2007 Knowledge transfers, between explicit and tacit, between individual and collective Serrat 2009 Explicit and systematic management of processes enabling vital individual and collective knowledge resources to be identiﬁed, created, stored, shared, and used for beneﬁt. Its practical expression is the fusion of information management and organizational learning Ramsin & Paige 2010 A framework for applying KM development practices and, like all methodologies, consists of two parts: process and modeling language Becerra-Fernandez & 2010 Performing the activities involved in discovering, capturing, Sabherwal sharing, and applying knowledge so as to enhance, in a cost effective fashion, the impact of knowledge on the unit’s goal achievement Pauleen & Gorman 2011 The application of knowledge management through individual strategies, based on experience and skills, to create maximum value for individuals Groff & Jones 2012 A set of organizational activities to achieving organizational objectives by making the best use of knowledge Clobridge 2013 The process of systematically capturing, describing, organizing, and sharing knowledge – making it useful, usable, adaptable, and re-useable Rouse 2013 An enterprise consciously and comprehensively gathers, organizes, shares, and analyzes its knowledge in terms of resources, documents, and people skills Chang & Lin 2015 A process of capturing, storing, sharing and using knowledge Navimipour & 2016 The process of capturing, sharing, developing, and using the Charband knowledge efﬁciently Table I. Liu, Wang et al. 2017 Not only managing tangible content from the literature but also Major deﬁnitions extracting information from the raw data available on about KM organization and systematization Kravchenko et al. (2017) designed a new approach for semantic similarity estimation to solve some problems about KM. They developed the genetic algorithm for semantic similarity estimation in accordance with the knowledge graph model. Xiao et al. (2016) proposed a new model for knowledge semantic representation (KSR) to produce semantic interpretable representations, which is used for explicitly representing knowledge. Che Cob et al. (2016) proposed a KM model based on semantic to support collaborative learning environment. Cob et al. (2015) discussed the application of SLN to enhance the KM and proposed a semantic KM model to support collaborative learning environment. Liu et al. (2014) IJCS Authors Year Description 2,1 Alavi & Leidner 2001 1. Storage or retention 2. Transfer or diffusion 3. Application or use Argote, McEvily & Reagans 2003 1. Creation 2. Retention 3. Transfer Arostegui 2004 1. Capture 2. Elaborate 3. Transfer 4. Storing 5. Share Lee et al. 2005 1. Creation 2. Accumulation 3. Sharing 4. Utilization 5. Internalization Chong & Choi 2005 1. Creating 2. Gathering 3. Organizing 4. Storing 5. Diffusing 6. Using 7. Exploitation Tikhomirova et al. 2008 1. Identiﬁcation and capture 2. Creation 3. Classiﬁcation and storage 4. Circulation and distribution 5. Application Huang & Shih 2009 1. Creation 2. Storage 3. Distribution 4. Utilization Turner, Zimmerman & Allen 2012 1. Creation or acquisition: 2. Storage 3. Dissemination or transfer 4. Application Clobridge 2013 1. Capturing 2. Describing 3. Organizing 4. Sharing Kanat & Atilgan 2014 1. Creation 2. Storage 3. Transfer Chang & Lin 2015 1. Capture 2. Store 3. Share 4. Use Hamoud et al. 2016 1. Creation 2. Internalizations 3. Acquisition 4. Reﬁnement 5. Utilization Table II. Navimipour & Charband 2016 1. Capture Different 2. Share descriptions about 3. Develop KM process 4. Use described the development of a semantic-based KM platform for Web-enabled environments Theoretical featuring intelligence and insight capabilities. conception Among the applications of SLN in KM, the most widely used method is ontology. Ontology was taken from philosophy, where it means a systematic explanation of being. An ontology is a catalog of existing concepts in a ﬁeld, which contains predicates, semantics of concepts and terms and how they relate to one another (Natalya et al., 2001). Ontology has wide application potential in the classiﬁcation of information, the construction of information and knowledge database, as well as the research and development of intelligent search engine. As shown in Table III, the applications of ontology to the ﬁeld of KM have aroused the concern of many researchers during the past decade. 3.2 Knowledge sharing One of the major challenges in KM is how to promote to share knowledge with others. In fact, effective KM relies on successful knowledge sharing (Swacha, 2015). Knowledge sharing can be deﬁned as “the exchange of knowledge between and among individuals.” And it aims at bringing knowledge sources together and manipulating into new knowledge structures or routines. Knowledge sharing and knowledge transfer are sometimes used synonymously or are considered to have overlapping content (Dan and Sunesson, 2012). Following the bulk of literature, we shall consider knowledge sharing to be semantically the same as knowledge transfer (Paulin and Suneson, 2012). The success of knowledge sharing relied on the degree to which the knowledge is recreated in the recipient. Swacha (2015) deﬁned a system of appropriate gamiﬁcation rules which makes use of a number of purposely selected gamiﬁcation components, and aimed at motivating individuals for various activities related to knowledge sharing. Yong (2013) provided new ﬁndings of the respective impacts of organizational rewards, reciprocity, enjoyment and Authors Year Description Arman, Hodgson & 2010 A framework of an ontology-based KM system including design and Gindy application at a real case which is developed in the Protégé environment and a generic system Hayette, Khaled, 2011 Designing a knowledge map ontology architecture that allows an Tahar et al. efﬁcient representation of knowledge to guide the users in the extraction of knowledge ZHENG et al. 2012 Proposing a new method for the construction of ontology-based agricultural KM system Loia, Fenza, Maio et al. 2013 Deﬁning a KM platform based on ontology that integrates methodologies aimed at supporting the life cycle of large and heterogeneous enterprise’s knowledge bases Pujara, Miao et al. 2013 An ontological information based method used for scaling knowledge graph identiﬁcation, jointly inferring a knowledge graph from the noisy output of an information extraction system Zhong, Fu, Xia et al. 2015 Ontology knowledge map is constructed to describe declarative knowledge and procedural knowledge Houhamdi & 2015 A knowledge description method using ontology and its application in Athamena multi agent systems Table III. Samwald, Giménez & 2015 An ontology-based framework that is capable to represent, organize Ontology for Boyce and reason over the growing wealth of pharmacogenomic knowledge knowledge Socaciu & Pascu 2016 A knowledge graph platform based on ontology using web ontology language and resource description framework to support KM representation social capital on individuals’ knowledge sharing intentions, which prior research has IJCS ignored so far. Their new ﬁndings will be very useful to deepening and widening our 2,1 understanding of the respective role of individual motivations and social capital in individuals’ knowledge-sharing intentions. Ma and Yuen (2011) proposed an online knowledge-sharing model and tested among undergraduate students using an online learning environment. And this model introduces two new constructs – perceived online attachment motivation and perceived online relationship commitment. Hung et al. (2011) investigated the effects of intrinsic motivation and extrinsic motivation on knowledge sharing in a group meeting. Results of their experiment showed that the KM system with built-in reputation feedback is crucial to support successful knowledge sharing. Tohidinia and Mosakhani (2010) evaluated the inﬂuence of a series of potential factors on knowledge- sharing behavior and suggested a systematic effort to improve knowledge-sharing behavior in organizations, an effort in which relevant factors from different perspectives are considered. 3.3 Performance measure for knowledge management Performance measurement is a crucial part in KM (Wang et al., 2015). By this process of measure, we can assess the effectiveness of KM practices and judge whether the current knowledge process can meet the our learning needs and whether it can provide feedback of information on KM to carry out continuous improvement on KM. KM performance evaluation includes the design of KM performance evaluation criteria and the selection of the evaluation methods (Wang and Zheng, 2010). This process consists of qualitative analysis and quantitative analysis. The common qualitative approaches for KM evaluation include open-ended questionnaires (Changchit et al., 2001), expert interviews (Booker et al., 2008), case studies and surveys (Darroch and McNaughton, 2002). While, the quantitative analysis is always used to measure the explicit knowledge with a series of indicators which include both ﬁnancial and non-ﬁnancial (Chen and Chen, 2005). Wang et al. (2016) proposed an index system of KM, which includes four components: the KM process, the organizational knowledge structure, the economic beneﬁts and the efﬁciency. Wang et al. (2015) categorized the performance measures into three categories: knowledge resources, KM processes, and the factors that affect KM. Zhang (2010) applied the Balanced Scorecard into the performance assessment of KM on the basis of the analysis of the Balanced Scorecard and KM and carried out the detailed analysis to measure the performance of KM tools from four aspects – ﬁnancial, customer, internal processes and learning and growth. Wang and Zheng (2010) proposed a KM performance evaluation method that includes knowledge system, structure capital, human capital, mental capital and market capital. Wu et al. (2009) developed an evaluation method of KM performance based on the principal component analysis. And the measure index consists of knowledge stocks, maturity degree of the learning organizations, information management and marketing capability. Tseng (2008) proposed a categorization matrix that classiﬁes the performance indicators for potential use in KM performance measurements. And the evaluation criteria of this method include process, human and IT. 4. 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(2010), “The application of the balanced scorecard in performance assessment of knowledge management”, The, IEEE International Conference on Information Management and Engineering, IEEE, pp. 443-447. Further reading Hau, Y.S., Kim, B., Lee, H. and Kim, Y.G. (2013), “The effects of individual motivations and social capital on employees’ tacit and explicit knowledge sharing intentions”, International Journal of Information Management, Vol. 33, pp. 356-366. Noy, N.F. and McGuinness, D.L. (2001), Ontology development 101: a guide to creating your ﬁrst ontology, Stanford University, Stanford, CA, 94305. Corresponding author Yueting Chai can be contacted at: email@example.com For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: firstname.lastname@example.org
International Journal of Crowd Science – Emerald Publishing
Published: Jul 10, 2018
Keywords: Knowledge management; Literature review; Design approaches
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