1.1 Motivation
In a knowledge-based organization, knowledge workers need to acquire a variety of knowledge (information) about their tasks [14]. Therefore, many organizations have built knowledge support platforms to assist workers in meeting their knowledge-needs. These platforms help workers to identify and share knowledge in order to speed up organization innovation and improve employee productivity [11, 25]. Studies on formulating knowledge-needs and streamlining knowledge provision are becoming more prevalent as the value of knowledge support keeps increasing [2, 38, 43, 49, 62-63].
The fast pace of technology evolution and the short cycle time for solving problems in current knowledge intensive environments has led to an emphasis on teamwork [16, 22].
For example, R&D activities often consist of many knowledge-intensive tasks that must be completed within a limited time period. These tasks are usually conducted through cross-function collaboration. By integrating the expertise and perspectives of various individuals, teams can quickly respond to interdisciplinary problems and enhance decision quality, thus providing a holistic solution. However, due to their individual task functions and roles, many team members have different knowledge-needs; as a result, they may expend considerable effort in seeking and synthesizing knowledge to obtain the required task-relevant knowledge [47, 65]. Reducing this expenditure of effort is one of the main challenges of collaborative knowledge support.
By mapping knowledge flows, organizations can provide task-relevant knowledge to workers that help them fulfill their knowledge-needs quickly and effectively [28]. A knowledge flow (KF) represents the flow of an individual’s or group members’
knowledge-needs and the referencing sequence of codified knowledge in conducting organizational tasks. Knowledge flows are an emerging topic of investigation in the knowledge management research field, and several studies have built knowledge flow models to illustrate knowledge sharing among knowledge workers [25, 29, 33, 40, 42,
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69-72]. For example, researchers in scientific fields who propose new ideas through content publishing form a knowledge flow in science [71]. The known ideas in one paper inspire new ideas for other researchers, and the established relationships or links generate a citation chain. Some studies have addressed knowledge sharing by defining the process in which knowledge is transferred from one team member to another [69-70, 72]. Other researchers have focused on discovering knowledge flows by analyzing workers’
knowledge-needs; the results have contributed to knowledge sharing in which the codified knowledge becomes available for recommendations to workers [28]. The shortcoming of these studies, however, is that the conventional models provide the same knowledge support to all team members; in other words, they do not consider the individual knowledge-needs that arise in a collaborative environment.
This dissertation proposes a novel knowledge-flow view (KFV) concept to consider workers’ knowledge-needs from different aspects, and demonstrates the benefits a cooperative team can receive while adopting them. The novel KFV models not only re-innovate conventional knowledge flow models but also enhance the efficiency of knowledge flow usage, as well as the effectiveness of knowledge sharing and knowledge support in teamwork environments.
1.2 Goals
Driven by the motivation, the dissertation aims to develop the models of knowledge-flow view to facilitate collaborative knowledge support, which fulfills teammates’ knowledge-needs from various aspects. Major goals of this work are listed below.
-Theoretically model base knowledge flows by adopting domain ontology to formulate knowledge-needs precisely.
-Develop an essential knowledge-flow view (KFV) model to derive virtual knowledge flows from a base knowledge flow in terms of task functions.
-Put roles in perspective to build role-based knowledge-flow view (r-KFV) model for
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addressing the relationships among roles, operations and knowledge requirements.
-Supply team participants with knowledge at required granularity to support task performance and team communication.
1.3 Approaches
This work extends previous knowledge flow research by exploring how to enhance conventional knowledge flow models to satisfy workers’ different knowledge-needs in teamwork environments. The challenges in a collaborative team are considerable and they pose many barriers to knowledge flows [31, 48]. Two of these barriers are low effectiveness and poor communication. Team members require different conceptual levels of knowledge to perform tasks and communicate with each other. For example, workers need specific knowledge to perform their tasks and general knowledge to communicate with other workers whose tasks or roles differ from their own. Effectively making collaborative knowledge provision at both specific level and general level is the key to team performance and productivities.
To formulate knowledge-needs precisely and model knowledge flows formally, this work first proposes a base knowledge flow (BKF) model which adopts domain ontology to describe knowledge-needs by the composition of knowledge concepts. According to the BKF model, a knowledge flow designer (KF designer) may either consult domain experts or investigate workers’ document access logs to identify participants’ knowledge-needs.
Thus, the collection of knowledge-needs and the order of referencing sequences would be used to construct base knowledge flows, which represent the knowledge-needs of participants by knowledge concepts.
In addition, since the BKF model does not consider personalized requirements and provides only one single view of a base knowledge flow, this would impact the effectiveness of knowledge provision while applying it in collaborative environments.
Therefore, by considering the different conceptual levels of knowledge in illustrating individual knowledge-needs, this work establishes an essential knowledge-flow view (KFV)
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model that aims to generalize knowledge concepts and derive virtual knowledge flows mainly from a task function perspective; as such, the essential KFV model would be capable of serving individuals’ knowledge-needs. A similar concept exists in database management systems, where administrators generate virtual database views from a base table to serve different purposes. A virtual knowledge flow (virtual KF) is derived dynamically from a base knowledge flow (base KF) according to the essential model which is employed to abstract knowledge concepts. The novel essential KFV model uses an order-preserving approach and a knowledge concept generalization mechanism to abstract some base knowledge nodes in a base KF, thus generating virtual knowledge nodes that correspond to the individual knowledge-needs of different workers [34].
Last, in real practice, tasks are often assigned to dedicated roles to ensure quality and security. Therefore, if a task involves teamwork, workers’ knowledge-needs will vary, depending on the roles they play [30]. For example, in a computer manufacturing company, a role of engineering is responsible for product development and another role of marketing designs strategies to launch and promotes new products. In this scenario, the engineering role needs a specific level of technical knowledge, but the marketing role only needs a general level of such technical knowledge to communicate with engineers. Thus, a role-based knowledge-flow view (r-KFV) model is required in the context, which includes the aspect of roles to apply knowledge management applications in teams [27]. The r-KFV model analyzes the conceptual levels of knowledge required by workers based on their roles, and develops role-based knowledge flow abstraction methods that generate virtual knowledge nodes to provide the appropriate level of knowledge for each role.
To investigate the feasibility of the proposed BKF and KFV models, a preliminary analysis was conducted. A case of mobile phone development and system design-related documents were illustrated and provided to several professionals to ask for their opinions about the feasibility of the proposed models from a practical perspective. Overall, there was general agreement with the feasibility of the KFV models. The agreement, to some extent, validates the feasibility of the approaches.
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In summary, this work addresses an important extension of knowledge flow research.
It considers a phenomenon that workers in teams usually have different knowledge-needs for task execution and team communication in terms of roles and task functions. The concept of knowledge-flow view and related models are proposed to support such knowledge-needs in teamwork environments.
1.4 Contributions
For a new discipline or a new research topic, theoretical papers are required to explore the basic theory by illustrating term definitions and establishing relationships between concepts [13, 19]. Thus, in order to explore the new topic – knowledge-flow view, this work is targeted as a theoretical research to establish BKF model, essential KFV model and r-KFV model for extending knowledge flow research in cooperative teams for organizational knowledge support.
This dissertation contributes to knowledge management development, first by showing how a knowledge flow can address knowledge-needs. The previous studies are lacking in illustrating knowledge flows in terms of workers’ knowledge-needs. The proposed BKF model fills this gap and helps researchers to obtain a clear view of knowledge flow research.
Additionally, this study investigates the shortage of knowledge support in collaborative teams because the workers in a team usually have different knowledge-needs according to their task functions. The essential KFV model is proposed to address the shortage. According to the essential KFV model, KF designers generate virtual knowledge flows that conceal confidential or detailed information base on workers’ task functions.
Through an order-preserving approach and a knowledge concept generalization mechanism, the virtual knowledge flows not only comply with organizational information security policy but also reflect the granularity of knowledge-needs. Thus, the essential KFV model can advance the applicability of knowledge flow research to cooperative knowledge support environments.
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The r-KFV model examines workers’ knowledge-needs in terms of their roles. The role represents a duty or a job position with the authority and responsibility to perform certain jobs within a team. So, it is essential to conduct knowledge provision in teams from a roles perspective. The r-KFV model design a kernel approach to derive role-based virtual knowledge flows from a base knowledge flow in role and operation perspectives. Based on role-operation knowledge requirement, the r-KFV model accurately illustrates roles’
knowledge-needs and effectively facilitates knowledge concept abstraction. It is an originative study of roles to address an important extension of knowledge flow research.
This work facilitates collaboration in teams by effective knowledge support. The innovative concept of knowledge-flow view and the proposed theoretical models can enhance the scope of knowledge flow research. In addition, this work also improves the efficiency of knowledge flowing, as well as the effectiveness of knowledge sharing and knowledge support in organizations.
1.5 Organization
Figure 1 shows the research framework including literature review in Related work part, model development and methodology design in Modeling part, and model evaluation in Preliminary analysis part.
Figure 1. Research framework.
The rest of this dissertation is organized as follows: Chapter 2 contains a review of
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related work. Chapter 3 builds a formal base knowledge flow (BKF) model. Chapter 4 defines and analyzes an essential knowledge-flow view (KFV) model. The algorithms to generalize knowledge concepts and derive virtual knowledge flows are described. Chapter 5 discusses the concepts of role-based knowledge-flow view (r-KFV) model and the methods of generating role-based virtual knowledge flows. Conclusions and future work are made in Chapter 6.
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