KNOWLEDGE MANAGEMENT SYSTEMS SUPPORT SECI MODEL OF KNOWLEDGE-CREATING PROCESS
Sreko Natek
International School for Social and Business Studies, Slovenia [email protected]
Moti Zwilling
Ariel University, Israel [email protected]
Abstract: Knowledge management (KM) has evolved from different disciplines and can be observed from various perspectives. The most important KM perspectives are personal communication and information technology (IT). Most IT companies have renamed their groupware and business intelligence products to fit the KM concept and to further develop them as knowledge management systems (KMS). In fact, they develop IT that can be used to implement or support KMS. Different IT can be used to develop successful KMS. The functionality and purpose of KMS are the most important criteria to define a system as KMS. The paper uses the SECI model of knowledge-creating process (Nonaka & Takeuchi, 1995) as a framework to propose suitable KMS and an important tool to manage the implementation of KM in contemporary organisations. Keywords: knowledge management, knowledge management system, knowledge-creating process, SECI model
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1. INTRODUCTION According to Jashaparas tree of KM disciplines, content and activity (Jashapara, 2011), KM has evolved from different disciplines and can be explored from various perspectives. Authors usually observe KM from at least two most important KM perspectives: personal communication and IT (e.g. Alavi & Leidner, 2001, Dalkir, 2011, Gomez & Perera & Manning, 2012, Hislop, 2009, Leonard, 1998). Alavi and Leidner define KMS as a type of information system to support the creation, exchange and application of knowledge, and the use of IT to support Nonakas SECI model (Alavi, 2001). Wang has expanded the concept of KMS with business intelligence and data mining to support KM processes: knowledge creation, storage, search, exchange and application (Wang, 2008). As the IT industry follows academic terminology trends, most IT companies have renamed their groupware and business intelligence products to fit the KM concept and to further develop them as KMS. In fact, they develop IT that can be used to develop or support KMS. Different IT can be used to develop successful KMS. The functionality and purpose of KMS are the most important criteria to define a system as KMS. The paper uses a model of knowledge-creating process, where knowledge is transformed between its tacit and explicit forms through four processes: socialisation, externalisation, combination and internalisation (Nonaka & Takeuchi, 1995), as a framework to propose suitable KMS and an important tool to manage the implementation of KM in contemporary organisations. Nonaka and other authors have recognised the described model as the SECI model and have moved from a concept of knowledge as substance to knowledge as a process in subsequent literature (Nonaka & Toyama & Hirata, 2008). 2. IT AND KM PROCESSES KMS are supported by different IT. The relationships are very different. IT can be used for KMS development. Sometimes specific IT is only used for one development phase or just to visualise results. KMS can be supported by nearly all the known IT. For example, an organisation can build a content management type of KMS with File Management System and Office tools. On the other hand, data mining technology can be used for knowledge discovery in rare data patterns or customer behaviour. Some IT is very useful only in a specific KM process (e.g. e-mail for knowledge sharing). A majority of KM authors try to clarify the role of IT in KMS and classify the available IT in relation to KM processes. In the digital era, KM is considered as a very important and popular compartment in understanding consumer's behaviour related to data that is gathered through computers. One demonstration could be viewed in the filled of recommendation systems: by building an automatic metadata engine to filter TV programs and recommend viewers on programs and related features. In this area, most of the IPTV set up boxes collect data on the viewer's habits. In addition a data mining engine scans and classify each of the parameters related to: programs, viewers and the preferences of viewers. The knowledge base created is used to form a knowledge discovery metadata which is used later to suggest upon video on demand and new programs to viewers. Becerra, Gonzales and Sabherwall classify IT according to KM processes (knowledge discovery, capture, sharing and application), KM systems, KM sub-processes and KM mechanisms (Becerra & Gonzales & Sabherwall, 2004). The matrix is very systematic but lacks clarity as much of IT is written several times (Picture 1). Picture 1: KM Processes, Mechanisms and Technologies
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Source: Becerra & Gonzales & Sabherwal, 2004, p. 41. Smith and Lyles have attempted to classify IT for KMS according to integrative or interactive nature of the available IT (Smith & Lyles, 2011). Both categories of IT can be used to support several KM processes and the distinction is not very clear. For example, groupware could be easily described as interactive IT (Picture 2). Picture 2: Key IT associated with KM
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Source: Smith & Lyles, 2011, p. 85. As described related to the IPTV viewers, collaborative authoring, discussion forums and real time interactions, are taken place in many systems, such as IPTV. In this smart TVs, the knowledge base related to viewers. The ability to evaluate their thoughts and behaviour, which is strongly influenced from social media viewers, could be analysed by data mining tools. The integration in expert systems can form a real time interaction engine which is able to suggest and evaluate which programs and video on demand could be offered to current, old and new TV viewers. The same authors have classified IT according to KM processes (Smith & Lyles, 2011). The list of IT is very short (Picture 3). The matrix is detailed enough to understand basic representatives of the available IT but not to guide a KMS development team in choosing the appropriate IT. Picture 3: IT support KM processes
Source: Smith & Lyles, 2011, p. 111. An excellent example of an overview model of complex KMS is provided by Laudon in Picture 4 (Laudon & Laudon, 2016). KMS in the centre of the model classify the available IT according to KM processes: knowledge acquisition, storage, dissemination and application, in parallel with management and organisational activities oriented into KM mechanisms (personnel- and organisation-oriented). The main benefit of the model is its active value chain orientation with a feedback loop to stress that KM is process- not IT-oriented. The key shortcoming of the model is its short IT list, not long enough for practical purposes. Picture 4: The KM value chain
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Source: Laudon & Laudon, 2016, p. 464. 3. THE SECI MODEL AS A KMS FRAMEWORK Knowledge creation is the most important KM process. Nonaka has developed a well-known and comprehensive SECI model of the knowledge-creating process (Picture 5). The SECI model is a wheel of tacit and explicit knowledge transformation, following four sub-processes: socialisation, externalisation, combination and internalisation (Nonaka, 2008). The model is precise enough to allow for a deep understanding of micro-processes and can be easily interpreted from the IT perspective. The essence of socialisation is knowledge sharing. The essence of externalisation is writing (codification) of knowledge. The essence of combination is storage, systemisation and processing of data, information and knowledge. Finally, the essence of internalisation is learning. Using these simple facts, the SECI model is precise enough for practical classification of the available IT for KMS development or support Picture 5: The knowledge creating process: SECI model
Source: Nonaka & Toyama, 2008, p. 19.
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New knowledge is an added value for all business, and especially KM, processes. Most of new knowledge is created through the externalisation of tacit knowledge to explicit knowledge in the form of knowledge articulation, writing or codification (all synonyms). In the process of externalisation, organisations and communities are learning. New knowledge is also created in the process of internalisation, transformation of explicit knowledge to tacit knowledge of a learning individual (Picture 6). New knowledge is also created through combination and socialisation (Jashapara, 2011). To classify the available IT to support KMS development, the SECI model matrix of the knowledge-creating process is used in Picture 7. IT for socialisation is socially oriented to support individuals, teams and participants in organisations or a society. The aim of socialisation KMS is to cross the borders of time and location, and to encourage people to communicate, share knowledge and enhance group decisions (Liebowitz, 2006). IT for externalisation is codifying-oriented to articulate and codify knowledge. Through externalisation KMS, knowledge can be used by more people, organisations and societies, thus ensuring better decision-making and problem-solving (Turban, 2011). IT for combination is typically data-, information- and knowledge-processing-oriented. Combination was traditionally the domain of IT, demonstrating its computer-processing power. The list of combination KMS is the longest list of the available IT, supporting data, information and knowledge processing, storage and retrieval. The most comprehensive data mining and artificial intelligence systems enable deep data and knowledge discovery based on huge amounts of data or ontology/taxonomy stored knowledge (Han & Kamber & Pei, 2012, Natek & Lesjak, 20
