The focus of this research targeted the technological and individual obstacles as noted by Riege through the collection of tacitknowledge into a KMS by those who possess the knowledgeusing a storytelling-basedapproach. As seen up to this point, the transfer of tacitknowledge through direct and indirect methods does occur; however, the elicitation of tacitknowledge into a KMS by those who have the knowledge is still considered a major challenge. Whyte and Classen (2012), in the Journal of KnowledgeManagement did collecttacitknowledge for a KMS using stories; however, it was via face-to-face interviews with the data later inserted, by the researchers, into the KMS. This is not an efficient approach as it takes much longer to conduct the interviews and then to insert the data into a KMS as well as make updates. This study proposed having the DE contribute their tacitknowledge into the KMS themselves with the vehicle of elicitation being stories. The literature either says it cannot be done (Fanfan, 2012) or it is done through interviews and recordings. In 2001, Swap, Leonard, Shields, and Abrams (2001) explored storytelling to transfer knowledge in the workplace and, like Whyte and Classen in 2012, they used interviews to collect the tacitknowledge (as did Schank (2010)). This research proposed using the KMS itself to collect the tacitknowledge with no intermediary.
In the Web 3.0 environment, tacitknowledgemanagementsystem could combine semantic web and artificial intelligence technology. Based on semantic analysis, the system can collect data from a user’s search and web browsing history in order to learn his interests and preferences. This allows the system to filter the search results and provide intelligent search results that he may be interested in. This knowledge acquisition mechanism significantly reduce users’ burden to repeatedly search and sort information, meeting one’s need to acquire knowledge.
The aim of our research was to set up and validate a risk assessment approach for organizations from the perspective of the knowledge life cycle and the knowledgemanagementsystem. The MARJETKA approach assesses risks related to explicit and tacitknowledge within the organization. Some limitations of the present research should also be noted. First, the case studies were limited to only 10 cases. Second, the selected case studies were concentrated in one country (Slovenia). Third, the initial risk rating is based on the subjective identification of knowledge threats and risks. Whilst these ratings are subjective, they probably represent the boundaries of this type or research. Although the MARJETKA approach was thus far only tried in 10 case studies, we demonstrated with a detailed discussion of two of those cases that it can be applied to, and is useful for, organizations of different sizes and types, whether commercial or in the public sector and therefor not-for-profit.
What do you think of using STMS as a mechanism to capture and share knowledge?
The purpose of this question is mainly to allow us to introduce STMS as a more effective system to share and capture experience- basedknowledge in other words TacitKnowledge. Before proceeding with the question, participants were given a clear idea of the workflow and content of the StoryTellling ManagementSystem. When this question came in, participants did not show quite an enthusiasm regarding the introduction of the STMS. Most Participants believe that the implementation the STMS requires quite some work and some time, but they do think that implementing could prove useful. One of the participants responded the following:
computer files, and many more (Uys, Du Preez et al. 2008). This unstructured information is only useful to an organization when its content can be automatically and electronically interpreted, grouped, and understood (Uys, Du Preez et al. 2010). Thus, physical objects must first be digitized in order to be electronically interpreted and to be of use to an organization. However, determining what a (set of) document(s) is about is not an easy task; traditional approaches such as analyzing documents and manually categorizing them are very time-consuming an labor intensive, and tools such as text summarization tools are largely ineffective (Uys, Du Preez et al. 2008). In addition, these difficulties are amplified when there is a large number of documents available and/or the locations of documents are distributed (Uys, Uys et al. 2008). According to Uys et al., three types of technologies can be helpful when working with large collections of natural language text. These technologies are information retrieval technologies/full-text searching software, clustering/classification technologies, and Natural Language Processing (NLP), data mining and visualization technologies (Uys, Du Preez et al. 2008). Firstly, information retrieval technologies or full-text searching software provide documents based on a user’s search query. The user types in a search query and the technology returns ranked sets of matching documents (Uys, Du Preez et al. 2008, Uys, Uys et al. 2008). This approach has several shortcomings, such as misspellings and different terminology that can lead to poor results (Uys, Uys et al. 2008). Moreover, this type of technology does not provide users with an overview of underlying concepts in a document collection (Uys, Du Preez et al. 2008). Secondly, clustering/classification technologies are mostly used to organize and group documents of a document collection (also named document corpus ) based on their content (Uys, Du Preez et al. 2008). Clustering can be defined as “the process of organizing things without the need to provide predefined categories” (Uys, Du Preez et al. 2010). With clustering, the categories are derived from the processed data. Contrasting to clustering, classification can be defined as “the process of organizing things using predefined
Chapter 2 provides the background of the case and clarifies which actors should be involved. The “Kasteelgracht” is a moat around “Het Kasteel", which is part of the residential area "Osseveld- Woudhuis” in the city Apeldoorn. During periods of drought, problems occur in the "Kasteelgracht" due to low water. If the water level decreases, the soil and plants dry out and this looks dirty and it leads to a bad smell. In addition, two exotic plants came into the “Kasteelgracht” spontaneously, which lead to a lack of visibility in the water. Furthermore, the weirs between different water levels are leaking and there will be some problems during heavy rain events. Due to a lack of the capacity in the sewerage will cause water to discharge via an overflow to the “Kasteelgracht”. When this happens parking spaces and sheds were flooded. All these problems are very annoying for the residents of the residential area. They had hindrance for 13 years and the water board Vallei and Veluwe and municipality Apeldoorn did never found the final solution for the problems. The problems were still not solved, because the involved actors had different perceptions about the problems and responsibilities. In addition, there was not enough budget and only a select group of persons within the water board and the municipality was involved in finding solutions. In order to find a solution for these problems a Working place was organized. In the Working place, all persons who had specific knowledge and experiences about the problem were invited. Finally, the Working place consisted of several persons of water board, the municipality and the residential council.
requirements have to be determined and suitable available concepts have to be benchmarked with these requirements. As already mentioned the knowledge-based NC programming system uses benchmarked features like MStep for structuring the work plan. Alternative work plan are usable to machine the same tasks. Therefore the requirements for a concept are handling time dependency, outlining different alternatives in a work plan and benchmarking operations (e.g. association of MStep). Furthermore, the application of traceable and well known algorithms for selecting / structuring and re-ordering alternative operation is required. Suitable and investigated knowledge representation and knowledge processing concepts known from the Artificial Intelligence (AI) are neural networks, formal rules-based languages and finally methods based on the graph theory (Görz, 2003). Neural networks are a set of “neurons”, which are organized in the input layer, processing layer and output layer. Neural networks are trained with special training algorithms. The disadvantage of this “black box” concept is that the neural networks follow processing in non traceable way (Görz, 2003). Therefore, the neural networks do not conform the requirements. Formal and rule-based languages are used to formalize a given well structured domain under discussion (Görz, 2003). Because of missing algorithms for processing structures like work plans this concept is not suited, too. The graph theory as last concept fulfils the given requirements. It is possible to model time dependency. Furthermore, the different paths will be utilized to represent the alternatives within the directed graph. By introducing “costs” for passing paths the benchmarking will be enabled. Finally the use of algorithms introduced in the graph theory like Floyd-Warshall (FW) algorithm and the TSP (Travailing Salesman Problem) algorithm enable the processing of the work plan. The methods from the combinatorics are used to transfer the task of generating an optimized work plan as operations research problem. Thus, a complete work plan consisting of a sequence of elements of MStep can be transferred in a directed graph. A directed graph DG is defined as an arrangement of a set of nodes (edges) V and a set of edges or arcs E. These arcs are connecting two nodes out of the set of nodes (Jungnickel, 1990). Regarding the work plan, the set of separate MStep can be transferred in the set of nodes. The arcs between the nodes will represent the ordering of these two MStep. An arc e = (a,b) means, that the MStep a is ahead of MStep b. Therefore time dependency of different elements
‘mood’ and provide a contagion beyond the conversation itself and the caucus of the team; and may reflect a collaborative nature salient beyond the team setting.
There has also been work examining how strategy discourse is used to legitimise and naturalise particular actions, activities and artefacts such as airline alliances in Vaara, Kleymann and Seristö (2004); or strategic plans in Vaara, Sorsa and Pälli (2010); and Samra-Fredericks (2005) examined how actors can use rhetorical skills to position themselves as knowledgeable in terms of strategic planning, enabling them to obtain power and authority. Here the focus is on strategy not as a noun (what an organisation is or has) but as a verb (strategize); what strategy does. This strategy as practice literature has used the idea of strategy as discourse to examine how dominant ideas about strategy come to influence how it is enacted in organisations. It may be argued that strategy conversation develops from the basis of the mental models already in place; therefore, any new discourse to an organisation will be interpreted in terms of what is already understood and accepted by the individuals within it. Peirce (Bergman 2009) would argue that actors do not create new knowledge, actors create new signs, and from this, a new narrative in which actors participate. Balanced scorecards are a means of agreeing the nature of the story, they collect signs, and therefore may define the nature of the language game. Language games are important to IC as IC is created through talk and actions between actors, characterised by continuous creation, modification, destruction and reconstruction of concepts. Narayanan and Fahey (1982) who cite organisations as political entities and coalitions, motivated by scarce resources, also suggest that analysis of the conversation and discourse around the decision-making process, in itself, is insufficient to explain how strategic choices are made and argue the need to investigate how strategies emerge, in order to understand how choices are made.
From the ancient period it is believed that capital, raw material and labor are the main source for creating and applying knowledge. At present knowledge is considered as an exceptional fund of indescribable economic resources and the dominant source of long-term competitive advantage (Ivona 2009). It is true that knowledge has been of decisive importance in the development of humankind (World Bank 2007). Knowledge is an important asset for countries as it provides potential for economic and social development by providing low cost and effective ways for service provision and production of goods while leading to globalization and competitiveness internationally (World Bank 2012).
Data are captured in most enterprises with the assistance of informal field sources. Tracker behavior and prior knowledge are involved: the tracker mentally records any information that seems to be interesting or surprising. He makes a selection on the basis of prior knowledge on which he relies and that is activated at that moment. In this way, as soon as it is captured, anticipative information loses its cohesion and becomes removed from its context. It becomes integrated with the tracker knowledge to enrich and to influence his knowledge. According to the assimilation / adaptation process, the recently acquired data and the previous tacitknowledge form the tracker’s new set of tacitknowledge. If a tracker shares the new information with a colleague, he might very likely present it not as such, but coated with commentaries derived from prior knowledge. Thus, the colleague in question does not merely receive the so-called information, but a richer perspective (in the sense of Daft and Lengel, 1986). The information flows orally and step by step through enterprise meetings. It can be “push” information, if the information’s possessor takes the initiative to talk about it, or more probably “pull” information if someone feels the need to request information.
Product design services for users, the user’s demand, awareness and evaluation for the product is very important, and these are the tacitknowledge which, at the beginning, must be understood by every designer to design products, which we called users’ tacitknowledge. Before the users know the product, "impression" will appear in the brain based on past experience and knowledge such as "what functions it should have,""what does it looks like " and other such the problem of 4W1H (what, when, who, where, how); when receiving product information, users typically make use of a certain adjectives, such as "normal", "delicate", "lightweight", "simple" to describe it  . Perceptual evaluation of these adjectives is composed to user tacitknowledge. At the same time, users have expressed tacitknowledge for product which is often the experience or ability of using product, it is particularly important for product innovation design.
The objectives of this paper are to elaborate the nature and attributes of the tacitknowledge. For the first objective, literature reviews from various scholars writing are analyzed to determine the nature and types of tacitknowledge. Twelve scholars’ writings are studied, representing various perspectives, such as organizational theorist, Nonaka, Baumard and Choo, philosophical views such as Polanyi and Collins, and behaviorist views such as Sternberg and Wagner, Aadne and Van Krogh. Besides the different perspective, these literature reviews are chosen based on the author’s contribution on the scholarly work on tacitknowledge. Polanyi is the first person who coined the word tacitknowledge. Collins and Wagner and Sternberg started the empirical research in tacitknowledge, Nonaka did an extensive research in knowledge creation, and popularized the SECI model. Baumard researched on knowledge in an organization context and introduce the concept of implicit knowledge. Choo, extends the work of Nonaka. Aadne started the research on social tacitknowledge, where as Van Krogh researched on tacitknowledge in relationship but maintains tacitknowledge is embedded in individual. Davenport and Prusak discusses knowledge in organizations.
broader KT research field, which tends to focus on indi- vidual clinicians or organizational-level supports; this study was designed to showcase the knowledge transla- tion occurring among teams. In terms of study implica- tions, the way in which explicit and tacitknowledge are integrated might be one of the most important aspects in the exploration of KT, and may shed light on new approaches to strengthening KT in public health. We might ask: where does tacitknowledge begin, and where and how does it overlap with explicit knowledge, if at all? What factors influence the interface between tacit and explicit knowledge and how do these vary during the program planning and evaluation cycle? This infor- mation can be used to determine when to encourage the appropriate use of tacitknowledge vis-à-vis explicit knowledge. For example, one might predict and then examine if tacitknowledge can support the contextual adaptation of explicit knowledge (e.g., research findings) so that they are relevant and applicable to local popula- tions and conditions. The key message arising from this work is that for more successful KT initiatives we ought to consider the role of tacitknowledge in intervention design, implementation protocols and/or in understand- ing the underlying mechanisms related to knowledge creation, dissemination and utilization.
experienced by public sector IT professionals due to the problems experienced in filling posts, competition between the private and public sectors for experienced IT employees, civil system rules and regulation focuses, control systems and political undercurrents. The findings confirm that the respondents do not receive adequate rewards for the job they do. As stated by Allen et al. (2008) and Döckel et al. (2009), a reward is ‘an incentive given to employees in return for their job involvement, performance and recognition, which is most wanted by employees’. Lockwood and Ansari (1999) contend that an attractive base salary, with the inclusion of a bonus and stock options, is important to IT professionals. They further point out that, for the most part, government salary classification systems are restrictive when compared with those offered by the private sector.
The second problem I encountered with coding was my own reluctance to add new categories to an already extensive list. However, I found when reviewing my coding that many of the factors that seemed wrongly coded were the ones that I had tried to fit into an existing risk category whose description did not really capture my understanding of the project manager’s comments. Once I overcame my reluctance to add new categories, the coding went smoothly. For example, a number of project managers identified the issue of team morale as a key risk factor, and a key problem that occurred during the course of their projects. Initially I considered coding this factor into Schmidt et al.’s risk category “Personnel: Poor team relationships”. The description given by Schmidt et al. is “Strains existing in the team due to such things as burnout or conflicting egos and attitudes.” Initially, it seemed to me that this could conceivably encompass strains due to low morale and motivation, especially when these have been caused by problems in the progress of the project or by excessive overtime required to meet deadlines. However, my respondents quite clearly saw the issue of morale and motivation as one quite separate from issues relating to conflicting egos and attitudes, and they also assigned a very high level of importance to the need to maintain morale as a risk mitigation strategy, and so I decided that a separate risk item was needed to capture these differences. My respondents also made distinctions between risks that related to their own firms and their own vendor project teams, risks that related to their clients and their client project teams, and risks that related to any third parties involved in the projects, and I needed to reflect these distinctions in the coding. In total, I created 45 new factors to more closely reflect the responses of the project managers that I interviewed. Sixteen of these factors were in the Relationship Management and Project Management themes, while a further fifteen specifically related to distinctions between vendor, third party and client risks.
transfer at Australian universities. Studies of other organisations (Aurum, Daneshgar & Ward 2008; Foos, Schum & Rothenberg 2006; Riege 2007) and the ministerial view (Bishop 2006) on universities reveal that there exists a research gap in understanding the enablers and inhibitors of tacitknowledge transfer. The lack of a particular mechanism for knowledge transfer, both explicit and tacit, has prompted the author to identify ways of tacitknowledge transfer by analysing knowledgemanagement enablers, inhibitors and processes that will aid in the creation, retention and distribution of tacitknowledge. This research will explore tacitknowledge transfer characteristics through surveys of academics in four Australian universities. It will explore and expand issues of knowledgemanagement adoption towards improving organisational processes in different universities as previous papers have limited themselves to a marginal sample and thus provide neither a comparison nor a single model for its adoption. The research will also explore how knowledgemanagement can be helpful in support of the sharing and creation of knowledge and how it can act as a catalyst for improved organisational processes. From both a research and applied perspective, there are negligible studies that focus on this topic especially ones that focus on tacitknowledge transfer within a university. Such a study would benefit research in tacitknowledgemanagement and also help to eliminate confusion as to where universities should focus their knowledgemanagement efforts for optimising performance and making tacitknowledge available for reuse.
Questions Graph and Schema were measures of explicit knowledge. The information necessary to answer these questions could be gathered from the product deliverable without any contextual information or tacitknowledge. With respect to these two questions, we observed a pattern in the answers to Graph not present or possible with Schema. We found that developers with superficial knowledge of the problem and their team’s implementation could get an inflated score without necessarily demonstrating knowledge of their actual system. That is, if the project had a user requirement such as “take survey,” and a student knew his or her team had all the major links on a main menu page, he or she could correctly guess and draw a link between a “main menu” node and a “take survey” node. Therefore, a high score on graph did not necessarily mean an individual actually had a working knowledge of the system. For this reason, and the fact that Graph was more subjective, we consider Schema to be a slightly better measure of explicit knowledge.