Modeling the knowledge-flow view for collaborative knowledge support
Duen-Ren Liu
⇑, Chih-Wei Lin
Institute of Information Management, National Chiao Tung University, No. 1001 Ta Hseuh Rd., Hsinchu 300, Taiwan
a r t i c l e
i n f o
Article history:
Received 11 January 2011
Received in revised form 16 January 2012 Accepted 25 January 2012
Available online 3 February 2012
Keywords: Knowledge flow Knowledge-flow view
Collaborative knowledge support Teamwork
Ontology
a b s t r a c t
In knowledge-based organizations, workers need task-relevant knowledge and documents to support their task performance. A knowledge flow (KF) represents the flow of an individual’s or group members’ knowledge-needs and the referencing sequence of documents in the performance of tasks. Through knowledge flows, organizations can provide task-relevant knowledge to workers to fulfill their knowl-edge-needs. Nevertheless, in a collaborative environment, workers usually have different knowledge-needs in accordance with their individual task functions. Conventional KF models do not provide workers with the different views of a knowledge flow that they require to meet these knowledge-needs. Several researchers have investigated KF models but they did not address the concept of the knowledge-flow view (KFV).
This study proposes a theoretical model of the KFV using innovative methods. Basically, a KFV is a vir-tual knowledge flow derived from a base knowledge flow that abstracts knowledge concepts for individ-ual workers based on their knowledge-needs. The KFV model in this study builds knowledge-flow views by abstracting knowledge nodes in a base knowledge flow to generate corresponding virtual knowledge nodes through an order-preserving approach and a knowledge concept generalization mechanism. The knowledge-flow views not only fulfill workers’ different knowledge-needs but also facilitate knowledge support in teamwork.
2012 Elsevier B.V. All rights reserved.
1. Introduction
In a knowledge-based organization, knowledge workers need to acquire a variety of knowledge (information) about their tasks[11]. Therefore, many organizations have built knowledge support plat-forms 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 pro-ductivity [9,20]. Studies on formulating knowledge-needs and streamlining knowledge provision are becoming more prevalent as the value of knowledge support keeps increasing[2,28,33,39, 49,50].
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[12,17]. 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 inte-grating 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, many team members have differ-ent knowledge-needs; as a result, they may expend considerable effort in seeking and synthesizing knowledge to obtain the re-quired task-relevant knowledge[37,52]. 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 knowl-edge-needs quickly and effectively [22]. A knowledge flow (KF) represents the flow of an individual’s or group members’ knowl-edge-needs and the referencing sequence of codified knowledge in conducting organizational tasks. Knowledge flows are an emerg-ing topic of investigation in the knowledge management research field, and several studies have built knowledge-flow models to illustrate knowledge sharing among knowledge workers
[20,22,26,30,32,55–58]. For example, researchers in scientific fields who propose new ideas through content publishing form a knowl-edge flow in science[57]. The known ideas in one paper inspire new ideas for other researchers, and the relationships or links established generate a citation chain. Some studies have addressed knowledge sharing by defining the process whereby knowledge is transferred from one team member to another[55,56,58]. Other researchers have focused on discovering knowledge flows by ana-lyzing workers’ knowledge-needs; the results have contributed to knowledge sharing whereby the codified knowledge becomes 0950-7051/$ - see front matter 2012 Elsevier B.V. All rights reserved.
doi:10.1016/j.knosys.2012.01.014
⇑ Corresponding author. Tel.: +886 3 5131245; fax: +886 3 5723792.
E-mail addresses:dliu@iim.nctu.edu.tw(D.-R. Liu),jwlin.iim93g@nctu.edu.tw (C.-W. Lin).
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Knowledge-Based Systems
available for recommendations to workers[22]. The shortcoming of these studies, however, is that the conventional models they adopt 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 study extends the previous research by exploring how to enhance conventional knowledge-flow models to satisfy workers’ different knowledge-needs. The challenges in a collaborative team environment are considerable and they pose many barriers to knowledge flows[24,38]. Two of these barriers are low effective-ness and poor communication. Team members with different task functions require different conceptual levels of knowledge in order to conduct their 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 differ from their own. By taking the different conceptual lev-els of knowledge into consideration when identifying individual knowledge-needs, this work proposes a knowledge-flow view (KFV) model that aims to generalize knowledge concepts and de-rive knowledge-flow views; as such, the model would be capable of serving multiple knowledge-needs. A knowledge-flow view is a virtual knowledge flow derived from a base knowledge flow, em-ployed to abstract knowledge concepts. The novel KFV model in this study uses an order-preserving approach and a knowledge concept generalization mechanism to abstract some knowledge nodes in a base knowledge flow, thus generating virtual knowledge nodes that correspond to the individual knowledge-needs of differ-ent workers. The proposed KFV model improves the knowledge support in collaborative teamwork environments and contributes to the literature on knowledge flow.
The rest of this paper is organized as follows: Section2provides a brief summary of related research. Section 3 builds a formal knowledge-flow model. Section4defines and analyzes a knowl-edge-flow view model and the algorithms to generalize knowledge concepts and derive knowledge-flow views. In Section5, a mobile phone development process is exploited to demonstrate the knowl-edge-flow view application. Finally, Section6draws conclusions.
2. Related work
Knowledge is one of the key assets to ensure sustained compet-itive advantage in the highly technological and global environment of modern organizations[16,25,40,46]. To achieve success in this environment, workers need to effectively apply knowledge to suc-cessfully conduct knowledge-intensive operations and manage-ment activities[8,28,56]. Knowledge management (KM) supplies the principles of creation, organization, transfer and application of the knowledge within organizations[20]and is recognized as a crucial practice for enabling organizations to survive in a knowl-edge economy era[51]. One purpose of KM is to support workers in fulfilling their knowledge-needs, by bridging the gap between workers’ knowledge and the requirements of various tasks
[2,45,49]. Studies have shown that precise and timely knowledge support is an important mechanism for increasing both productiv-ity and work effectiveness[21,28].
In a task-based business environment, tasks are conducted in work processes and the effective provision of task-relevant knowl-edge and context information is crucial to increasing workers’ pro-ductivity. To meet this provision, solutions which integrate information retrieval (IR) and workflow management systems (WfMS) have been developed. These solutions proactively deliver task-relevant knowledge according to the context of tasks[1,33]. For example, the KnowMore system derives task profiles from pro-cess definitions that facilitate knowledge provision[1]. The Flow-Wiki system was developed by a wiki-based approach for agilely
managing workflows and effectively providing relevant informa-tion to participators[19]. In this way, process participants can ob-tain knowledge that perob-tains to task profiles and/or the execution context of the current process.
To fulfill workers’ knowledge-needs, knowledge flows provide links among knowledge sources. Through knowledge flows, work-ers can effectively obtain knowledge from these sources to execute tasks[20]. Knowledge flows illustrate the sequence of knowledge-needs and/or the order of referring documents when workers per-form tasks. Knowledge flows can facilitate knowledge sharing and reuse in both business and research environments. For example, Zhuge[56]illustrated a knowledge flow within the software devel-opment team of a distributed organization. Here, the knowledge flow carried and gathered knowledge from one team member to another for sequential knowledge sharing. Similar knowledge shar-ing can take place in a citation chain where knowledge is trans-ferred among scientific researches. In this context, the citation chain of papers is a knowledge flow that disseminates knowledge among scientists and inspires new ideas[57].
Several knowledge-flow models have been built in recent re-searches. Luo et al. [30] modeled a Textual Knowledge Flow (TKF) from a semantic link network. The purpose of the TKF was to recommend proper browsing paths to users after evaluating their interests and inputs. Lai and Liu[22] constructed a time-ordering knowledge flow model to illustrate the sequence of work-ers’ knowledge referencing behaviors. In this model, workers ob-tained proper knowledge to fulfill their knowledge-needs through the knowledge flows discovered in document access logs. Kim et al.[20]proposed a knowledge flow model using a process-oriented approach to capture, store and transfer knowledge. Zhang et al.[53]used Petri-Net to model a knowledge flow. In this model, a knowledge node was used to generate, learn, process, under-stand, synthesize and deliver knowledge based on four types of flow relations: creation, merging, replication and broadcasting. Zhao and Dai[54]integrated business processes and knowledge flows and divided knowledge flows into sequence, distribution, combination and self-reflection patterns based on RAD (role-activ-ity-diagram). Finally, Anjewierden et al.[4]suggested that the ref-erencing sequence in weblogs may be regarded as a knowledge flow and can be described as a sender-message-receiver model.
Both knowledge flow and knowledge-based planning prompt similar ideas about embedding knowledge while building models. Knowledge-based planning is a planning methodology used to identify a sequence of tasks executed by one or more agents under given initial conditions and resource constrains to achieve final goals[5]. The methodology involves knowledge acquisition, knowl-edge validation and knowlknowl-edge maintenance of planning domains, and adopts appropriate knowledge-based planning tools to build planning models[5]. For example, R-Moreno et al.[36]successfully utilized a planning and scheduling system as well as a workflow modeling tool to plan a telephone installation workflow model. The workflow modeling tool was used to acquire relevant knowl-edge, such as initial conditions, resource constrains and final goals; then the planning and scheduling system was used to convert the knowledge into planning standard expressions. A knowledge-based planning system can also be employed to manage the result of planned tasks for the purpose of fulfilling other tasks’ precondi-tions. Chow et al.[10], for example, proposed a strategic knowl-edge-based planning system (SKPS) that combined knowledge rules with mathematical models to formulate co-loading shipment plans. Through SKPS, shipment planners could acquire, validate and maintain knowledge of the shipment domain, and thus build a co-loading shipment planning model so that executors could utilize the knowledge in the model to perform tasks efficiently.
As the above examples demonstrate, knowledge-based plan-ning focuses on building planplan-ning models for problem solving or
task execution. Knowledge flow research contributes to the build-ing of knowledge-flow models for correspondbuild-ing task execution plans (or workflow processes) that support knowledge provision, sharing and transferring[22,56]. Knowledge flows can be either derived by mining workers’ access logs[22]or specified by knowl-edge-flow modelers according to their experience in executing the corresponding workflow process[55,58]. Besides these two meth-ods, knowledge-based planning tools can complement knowledge flow research by helping modelers build the appropriate knowl-edge flows that correspond to task execution plans.
Ontology is a widely accepted approach for capturing and rep-resenting knowledge possessed by an organization[34,36,43]. It is a conceptualization mechanism that defines knowledge concepts in a specific domain and constructs a hierarchical structure to de-scribe their inter-relationships[14]. Ontology can promote a com-mon understanding throughout a whole organization to facilitate knowledge storage, retrieval and synthesis[35]. For example, the common terminologies and knowledge concepts in an ontology can improve the problem-solving capability and efficiency within a supply chain[6]. Another example of ontology pertains to the knowledge concepts derived from Wikipedia articles and catego-ries, which can be used to predict the contents of documents
[44]. Weng and Chang[47]proposed a research document recom-mendation system which exploited ontology to construct user pro-files, and utilized the profiles to illustrate researchers’ interests. Afacan and Demirkan[3]developed an ontology-based universal design support system to support designers in the conceptual de-sign phase; it adopts ontologies to process and represent required knowledge. As the above examples illustrate, ontology is a versatile paradigm that can be applied in many domains.
Recently, business process modeling has been rapidly applied to streamline business administration and to facilitate cooperation among enterprises. Business process modeling refers to the design, analysis and execution of business processes[15]. Its goals are to describe a set of activities that can be performed in sequence, and to allocate resources and arrange jobs optimally by analyzing the organizational and technical environments[48]. By employing appropriate modeling tools, business process modeling can provide pre-defined templates that allow enterprises to enact their busi-ness processes in an effective and efficient manner.
In an industrial environment, processes describe the flows of business operations. Workflow management systems are defini-tion and execudefini-tion tools that support these operadefini-tions [35]. In practice, participants involved in a workflow need a flexible work-flow model capable of providing appropriate process information
[2,27]. Because of the increasing complexity of business processes and the variety of participants, it is beneficial for organizations to define virtual processes with different views of the workflow
[7,13,27,42]. Liu and Shen[27]presented a novel concept of pro-cess abstraction: the propro-cess-view. A propro-cess-view is an abstracted process derived from a base process to provide generalized process information. The process-view is generated by an order-preserving approach, which ensures that the original order of the activities in the base process is preserved. This paper adopts similar ideas to generate knowledge-flow views from a base knowledge flow, while retaining the knowledge referencing order.
3. Knowledge-flow model: a base knowledge flow
In cooperative working environments, a knowledge flow (KF) represents the flow of team members’ knowledge-needs and the referencing sequence of codified knowledge that workers need while conducting business processes or research tasks. For exam-ple, a mobile phone development process consists of multiple tasks which require joint efforts from Marketing, Design, Outsourcing,
Quality Assurance and Sales departments. Participants not only contribute their expertise, but also refer to additional codified knowledge that contributes to the performance of tasks in pro-cesses. The flow of knowledge-needs and the sequence of docu-ment reference can be represented by a knowledge flow. Fig. 1
shows a mobile phone development process consisting of nine tasks: business analysis, industrial design, major parts identifica-tion, parts sourcing, hardware design, platform setup, application design, verification and commercialization.
In the above process, team members may have the knowledge-needs of Marketing segmentation and Consumer analysis while con-ducting the Business analysis task. The knowledge concepts rele-vant to the knowledge-needs include: Geographic segmentation, Psychographic segmentation, Consumption environment and Con-sumer behavior. Knowledge-flow modelers put these knowledge concepts into a knowledge node to represent the knowledge-needs of the Business analysis task. In addition, the team members may also have the knowledge-needs of accessing Compliance guidance and Usability checklist, two related knowledge concepts, while per-forming the Verification task[18]. The knowledge of how to build knowledge flows is derived from structured interviews and work-shops[20], system event logs[22], as well as the content of tasks. For example, in investigating the whole business process, knowl-edge-flow modelers rely on their experience [55,58], interviews of domain experts[20], and/or the analyses of workers’ document access logs[22,26]to collect knowledge-needs on a task-by-task basis. These knowledge-needs are illustrated by knowledge con-cepts which are identified by domain ontology. By using domain ontology, the knowledge-flow modelers group relevant knowledge concepts into corresponding knowledge nodes to form a knowl-edge flow.
Fig. 2shows the corresponding knowledge flow of the mobile phone development process. In the knowledge flow, for example, the knowledge concept Consumer behavior is related to market trends research and customer preferences investigation, which facilitate marketing staff and designers in identifying major parts such as display, battery and cards options by evaluating their com-binations. Accordingly, the knowledge-flow modelers group the relevant knowledge concepts Consumer behavior, Display options, Battery options and Card options to form the knowledge node k2 to represent the knowledge-needs of conducting the major parts identification task.
A knowledge flow that represents knowledge-needs and a refer-ence sequrefer-ence is herein termed a base knowledge flow. In this sec-tion, we formally define domain ontology and base knowledge flow for the purpose of building a theoretical knowledge-flow model. Definition 1 models domain ontology, which is the infrastructure for sharing knowledge concepts throughout the whole organiza-tion. Definitions 2–7 formulate the knowledge-flow model. Definition 1 (Domain ontology). Ontology is constructed to define knowledge concepts and their hierarchical relationships in a domain.
Ontology is defined as O = hC, HRi, where C is a set of knowledge concepts derived from a specific domain. HR is a set of hierarchical relations which define the parent-child relationships among knowledge concepts in C, and HR is formally expressed by HR = {hr|hr
e
C C}.For two knowledge concepts, x and y, if x has a downward link to y (or y has an upward link to x) in an ontology, then x is the par-ent concept of y and y is the child concept of x. Two semantic rela-tions, Generalization and Specialization, are used to describe the relative conceptual level of two knowledge concepts. Relations be-tween the parent concept x and the child concept y are formally
ex-pressed by Specialization (x) = {y|y is a child concept of x} and Gen-eralization(y) = {x|x is a parent concept of y}.
Fig. 3shows an example of ontology in the mobile phone devel-opment domain. The root of the ontology is Mobile Phone Develop-ment. It represents the most general knowledge concept, as indicated also by R&D strategy and the product development guideline. Six subconcepts, Marketing, Industrial design, Hardware design, Software design, Quality verification and Sales appear under Mobile Phone Development. Likewise, Market segmentation, Con-sumer analysis and Outsourcing are the subconcepts of Marketing. Hence, Specialization (Marketing) = {Market segmentation, Consumer analysis, Outsourcing} and Generalization (Market segmentation) = {Marketing}.
In existing research[23,29,31,41,43], the relations of ontologies are designated as: is-a, part-of, subclass, synonym or related-to. The meanings of these relations pertain to the design purpose of ontol-ogies and the characteristics of knowledge concepts. In this work, ontology is designed to represent knowledge concepts and their hierarchical relationships in a domain. The use of ontology facili-tates the abstraction of knowledge concepts based on their concep-tual levels, which are required for building knowledge-flow views. Two semantic relations, Generalization and Specialization, are used to describe the relative conceptual levels of two knowledge con-cepts without distinguishing between the meanings of relations such as is-a, subclass, synonym or related-to. According to the Generalization relation, child (specific) knowledge concepts can
Hardware Design Major Parts Identification Verification Business Analysis Application Design Parts Sourcing Industrial Design Platform Setup Commercialization
Fig. 1. Mobile phone development process.
k3 Design rule of RF and Baseband 1. Consumer behavior 2. Display options 3. Battery options 4. Card options 1. Compliance guidance 2. Usability checklist 1. Geographic segmentation 2. Psychographic segmentation 3. Consumption environment 4. Consumer behavior 1. Multimedia functions 2. Internet access options 1. Hardware design alternatives 2. Price/performance of parts 3. Service level agreement of 3rd parties 1. Portability 2. Customized features 3. Human factors Features of iOS and Android 1. Pricing strategy 2. Direct channel selection 3. Operators bundling k0 k1 k2 k4 k5 k6 k7 k8
Fig. 2. Knowledge flow of the mobile phone development process.
Pricing Strategy Distribution Channel Consumer Beha vior Consumption Environment Usa bility Porta bility RF Ba seba nd Displa y Card Reader Ba ttery Internet Access Multi Media Marketing
Mobile Phone Development
Industrial Design Hardware Design Software Design
Consumer Analysis Sales Psychica l Percept Customized Fea tures Human Fa ctor IC Components Mechanical Parts Operation System iOS Android Application Outsourcing Service Level Agreement Price/ performa nce Psychogra phic Segmenta tion Market Segmentation Geogra phic Segmenta tion Competitive Pricing Cost-plus Pricing Direct Cha nnel Operators Quality Verification Certifica tion Test Usa bility Eva lua tion
Complia nce Guidance Industria l Regula tions Result Mea surement Usa bility Checklist
be abstracted to parent (general) knowledge concepts. For exam-ple, the knowledge concept Operation system contains knowledge about categories and functions of APIs. Knowledge concept iOS contains knowledge of detailed specifications of Apple iOS’s APIs. Thus, these two knowledge concepts are related and on different conceptual levels. The knowledge concept Operation system com-prises more general knowledge than does knowledge concept iOS. Thus, knowledge concept Operation system is on a higher con-ceptual level than knowledge concept iOS. The same relation exists between knowledge concepts Operation system and Android. As shown inFig. 3, knowledge concepts iOS and Android have upward links to knowledge concept Operation system. The relations among them can be expressed as Generalization (iOS) = {Operation system} and Specialization (Operation system) = {iOS, Android . . .}.
Based on domain ontology, knowledge-flow modelers can for-mulate workers’ knowledge-needs by using combinations of knowledge concepts on different conceptual levels. For example, workers with knowledge-needs about market segmentation might identify and divide potential consumers into groups according to their characteristics, behavior and location. In this example, knowl-edge-needs can be represented either by the knowledge concept Market segmentation or by two knowledge concepts, Geographic seg-mentation and Psychographic segseg-mentation. The knowledge concept Market segmentation is a general concept. As such, it is used to de-scribe the purpose of segmentation or to introduce the guideline of selecting one segmentation alternative among others. By con-trast, the knowledge concepts, Geographic segmentation and Psycho-graphic segmentation, are specific concepts that describe detailed knowledge pertaining to the steps used in analysis or the segmen-tation criteria. Market segmensegmen-tation is the parent (general) concept of Geographic segmentation and Psychographic segmentation, whereas Geographic segmentation and Psychographic segmentation are the child (specific) concepts of Market segmentation.
As this example shows, workers’ knowledge-needs can be ex-pressed as a combination of knowledge concepts in domain ontol-ogy, where the conceptual levels of these knowledge concepts can be identified by their positions in the ontology. By grouping knowl-edge concepts at the proper conceptual levels, knowlknowl-edge-flow modelers can use domain ontology as a reference base to identify workers’ knowledge-needs. Furthermore, domain ontology can facilitate the abstraction of knowledge concepts, which are re-quired for generating knowledge-flow views.
Next, we formulate the knowledge-flow model through a series of definitions provided below:
Definition 2 (Knowledge node). A knowledge node x is a set of knowledge concepts needed by workers to fulfill their tasks. The knowledge concepts of x are denoted as KC(x) = {c1, c2, c3, . . . , cm} where knowledge concept cican be identified by domain ontology.
Definition 3 (Dependency). A dependency is an ordered pair (knowledge node x, knowledge node y) denoted by dep(x, y). This notation indicates that after knowledge concepts in x have been referenced, workers can start to reference the knowledge concepts in y. In dep(x, y), x is called the preceding node and y is called the succeeding node.
Definition 4 (Knowledge flow). A knowledge flow (KF) is a 2-tuples hKNS, DSi, where KNS is a nonempty set, and its members are knowledge nodes in the knowledge flow. DS is a nonempty set, and its members are dependencies that preceding nodes and suc-ceeding nodes are in KNS.
Definition 5 (Neighboring). Two knowledge nodes are neighboring if a dependency between them exists in DS.
Definition 6 (Path). Given a knowledge flow KF = hKNS, DSi, a path is defined to include a starting knowledge node k0, intermediate knowledge nodes k1, k2, . . ., kn1, an ending knowledge node kn and a set of dependencies, dep(ki1, ki) 2 DS, for i = 1, 2, . . ., n. The path from k0to knis denoted by k0?kn.
Definition 7 (Ordering relation). Given a knowledge flow KF = hKNS, DSi and two knowledge nodes x, y 2 KNS, knowledge node x has a higher order than knowledge node y if a path x ? y exists. The ordering relation is denoted as x > y.
4. Knowledge-flow view model: a virtual knowledge flow By knowing what other members know, a team is able to gain better decision quality and communicate more effectively [37]. Therefore, team members not only need specific knowledge to conduct the tasks assigned to them, but also require general knowledge about tasks performed by other members to facilitate their communication. For example, in the mobile phone develop-ment process, marketing staff members refer to specific geo-graphic segmentation documents to identify possible consumer groups, and they gather specific knowledge of consumer behavior to determine the acceptance level of a new mobile phone. In addi-tion, they need general knowledge related to Industrial design, Hardware design, Software design, Quality verification and Sales to communicate with members outside their departments through the use of common terminology. The knowledge support of both specific and general knowledge pertaining to different tasks can assist marketing staff members to complete their business analy-sis task and increase the communication quality of the team. However, since conventional knowledge-flow models provide only a single view of a knowledge flow and do not consider personal-ized requirements, they are not applicable in such environments. In fact, project managers do not need specific and detailed knowl-edge about Business analysis, Industrial design, Hardware design and other tasks. They only need general knowledge of these tasks to help them make decisions and communicate with other team members. Fig. 4 shows a knowledge-flow view with general knowledge concepts that can meet project managers’ knowl-edge-needs.
The knowledge-flow view inFig. 4includes three virtual knowl-edge nodes: vk1, vk2 and vk3, which represent the knowledge-needs of project managers in the mobile phone development pro-cess. The virtual knowledge node vk1 consists of two general knowledge concepts: Market segmentation and Consumer analysis, which project managers require to oversee the business analysis task. These two general knowledge concepts are abstracted from four specific knowledge concepts: Geographic segmentation, Psycho-graphic segmentation, Consumption environment and Consumer behavior. In node vk2, which represents product design-related knowledge concepts at the general conceptual level, the three gen-eral concepts: Industrial design overview, Hardware design overview and Software design overview are more helpful to project managers in communicating with product designers than the concepts from which they are abstracted. Finally, the general knowledge concepts in node vk3 are advantageous to project managers in overseeing Verification and Commercialization tasks; hence, the knowledge-flow view inFig. 4appropriately formulates the knowledge-needs of project managers in the development process, and illustrates corresponding knowledge concepts at the proper conceptual levels. The formal model of the knowledge-flow view (KFV) can now be defined: Knowledge-flow views are the abstracted forms of a base knowledge flow, and are herein referred to as virtual knowl-edge flows. Since knowlknowl-edge-flow views are abstractions, different
knowledge-flow views can be generated based on individual par-ticipants’ knowledge-needs and organization policies. By providing different knowledge-flow views that hide all or some of the de-tailed information in a base knowledge flow, organizations can be better equipped to enforce policies.Fig. 5shows an example of mapping a base knowledge flow to multiple knowledge-flow views. While product managers do not need to have detailed knowledge of all the knowledge concepts in the knowledge flow, they must have general marketing knowledge to understand mar-keting trends and to increase communication effectiveness within a team. To serve product managers’ needs, knowledge-flow modelers can abstract marketing-related knowledge nodes and generalize knowledge concepts in those nodes to hide detailed marketing information. A possible knowledge-flow view for prod-uct managers is as follows: knowledge nodes k1 and k2 are ab-stracted to a virtual knowledge node vk1, and k3, k4, k5and k6are abstracted to vk2. In addition, manufacturers have their own knowledge flow view which contains specific manufacturing knowledge (represented by vk2), general knowledge of marketing and design (represented by vk1), as well as general knowledge of sales and post service (represented by vk3). As this illustrative
anal-ysis shows, a knowledge flow can be abstracted to multiple knowl-edge-flow views by considering different knowledge-needs and organization policies. In this way, workers can obtain the proper knowledge-flow views that help them acquire the knowledge sup-port they need in collaborative environments.
4.1. The formal framework of the knowledge-flow view model Definitions 8–13 describe the properties and basic terms that constitute the theoretical framework of the knowledge-flow view model.
Definition 8 (Concealing criteria). A concealing criterion is a 3-tuples hworker w, knowledge node kn, boolean of abstraction Y/Ni, which states whether the knowledge concepts of a knowledge node kn are too specific or confidential for a worker w’s task functions. If the answer is yes, the boolean of abstraction is set to Y, and the knowledge concepts of kn are abstracted. On the other hand, if the knowledge concepts of kn are appropriate for w’s task functions and need not be abstracted, the boolean of abstraction is set to N.
1. Market segmentation 2. Consumer analysis
1. Industrial design overview 2. Hardware design overview 3. Software design overview
1. Quality verification overview 2. Sales overview
3. Distribution channel evaluation
vk1 vk2 vk3 Knowledge-flow view Knowledge flow Design rule of RF and Baseband 1. Consumer behavior 2. Display options 3. Battery options 4. Card options 1. Compliance guidance 2. Usability checklist 1. Geographic segmentation 2. Psychographic segmentation 3. Consumption environment 4. Consumer behavior 1. Multimedia functions 2. Internet access options 1. Hardware design alternatives 2. Price/performance of parts 3. Service level agreement of 3rd parties 1. Portability 2. Customized features 3. Human factors Features of iOS and Android 1. Pricing strategy 2. Direct channel selection 3. Operators bundling
Fig. 4. A knowledge-flow view for project managers.
k
2k
4k
1k
3k
5k
6Knowledge flow
Knowledge-flow views
various views on the
same knowledge flow
vk
1vk
2product manager
vk
1vk
2manufacturing
vk
3The concealing criteria are defined by knowledge-flow model-ers to comply with the company’s information security control rules and fulfill team members’ need-to-know requirements. The knowledge-flow modelers can refer to their experience or utilize experts’ knowledge to discover what knowledge nodes should be abstracted if the knowledge concepts in the knowledge nodes are too specific or confidential for workers to perform their task func-tions properly.
Two scenarios illustrate how the knowledge-flow modelers de-fine concealing criteria when deriving VKFs from a base KF for a sourcing planner (denoted as p for short). The sourcing planner oversees the management of outsourced parts, including: survey-ing reliable suppliers, evaluatsurvey-ing price/performance of parts and negotiating service level agreements with suppliers. The base KF (shown inFig. 2) includes two knowledge nodes, k2and k4, which contain the required knowledge concepts to conduct two tasks: major parts identification task and hardware design task, respec-tively. In the first scenario, the concealing criterion is made by the knowledge-flow modelers, based on information security con-trol rules. In order to sustain their competitive advantage, many companies enforce information security policies to protect their precious intellectual properties, such as hardware design specifica-tions, from unauthorized access. Only work-related employees can access such specific knowledge or information. The sourcing plan-ner’s tasks are not directly related to hardware design tasks, so p is not allowed to access the knowledge concept Design rule of RF and Baseband which is one type of hardware design specification. Be-cause the knowledge concept Design rule of RF and Baseband is in-cluded in k4and p is not allowed to access it, the knowledge-flow modelers define a concealing criterion hp, k4, Yi while deriving VKFs for p. The concealing criterion hp, k4, Yi indicates that k4’s knowledge concept Design rule of RF and Baseband needs to be ab-stracted to a general knowledge concept, IC components, based on the domain ontology (as shown inFig. 3). The concealing criterion not only protects the specific knowledge concept Design rule of RF and Baseband from unauthorized access, but also provides general knowledge concept IC components for p to effectively communi-cate with other team members.
In another scenario, the concealing criterion is made in terms of workers’ need-to-know requirements. Supposing that the knowl-edge concepts consumer behavior, display options, battery options and card options in k2are too specific for p to conduct his/her tasks. Consequently, the knowledge-flow modelers define a concealing criterion hp, k2, Yi to reflect p’s knowledge-needs when deriving VKFs for p. The concealing criterion hp, k2, Yi indicates that k2’s knowledge concepts need to be abstracted to the general knowl-edge concepts consumer analysis and mechanical parts, respectively, based on the domain ontology (as shown inFig. 3). The two scenar-ios show that the knowledge for defining concealing criteria is practical and context-dependent, depending on the consideration of security as well as the knowledge-needs of the participants. Definition 9 (Virtual knowledge node). A virtual knowledge node consists of a set of knowledge nodes and corresponding knowledge concepts. The knowledge concepts of a virtual knowledge node are abstracted from the knowledge concepts of the corresponding knowledge nodes. A virtual knowledge node vx is a 2-tuples hANS, AKCi, where ANS (Abstracted Knowledge Node Set) is a nonempty set and its members are knowledge nodes or previously defined virtual knowledge nodes. AKC (Abstracted Knowledge Concept Set) is a nonempty set and its members are knowledge concepts defined in domain ontology.
The knowledge concepts of vx are denoted as AKC
(vx) = {c1, c2, c3, ..., cq} where knowledge concept cican be identified in domain ontology.
Definition 10 (Virtual dependency). Given KF = hKNS, DSi and two virtual knowledge nodes, vx and vy, a virtual dependency vdep(vx, vy) from vx to vy exists if dep(x, y) is in DS, where x is a member of vx and y is a member of vy. A virtual dependency is used to connect two virtual knowledge nodes, vx and vy.
Definition 11 (Virtual knowledge flow). A virtual knowledge flow (VKF) is a 2-tuples hVKNS, VDSi, where VKNS is a nonempty set and its members are virtual knowledge nodes, and VDS is a non-empty set and its members are virtual dependencies. A knowl-edge-flow view is herein termed a virtual knowledge flow. Definition 12 (Virtual Path). Given a virtual knowledge flow VKF = hVKNS, VDSi, a virtual path in VKF, extending from vk0 to vkn, is a sequence of virtual knowledge nodes vk0, vk1, vk2, ..., vkn2 VKNS, such that vdep(vki1, vki) 2 VDS for i = 1, 2, . . . , n. The virtual path from vk0to vknis denoted as vk0?vkn.
Definition 13 (Virtual ordering relation). Given a virtual knowl-edge flow VKF = hVKNS, VDSi and two virtual knowlknowl-edge nodes: vx and vy 2 VKNS, vx has a higher order than vy if a virtual path vx ? vy exists. The virtual ordering relation is denoted as vx > vy.
Fig. 6illustrates the relationship between the components of the novel model. As the figure shows, a knowledge-flow view is an abstraction from a base knowledge flow. The abstraction rela-tionships exist in major components. Virtual knowledge nodes are abstracted from knowledge nodes; thus, a virtual knowledge node contains generalized knowledge concepts that are abstracted from the knowledge concepts in corresponding knowledge nodes. Both the abstracted knowledge concepts and the concepts from which they are abstracted can be identified in the domain ontology.
4.2. An order-preserving approach for deriving a knowledge-flow view Liu and Shen[27]presented an order-preserving approach to the generation of virtual processes from a base process in workflow environments. The approach is designed to ensure that the original ordering relation of activities in a base process is preserved in vir-tual processes. This paper adopts the order-preserving approach for the purpose of generating knowledge-flow views from a base knowledge flow, that retain their knowledge referencing order in the base knowledge flow. A legal virtual knowledge node must fol-low three basic rules to preserve the ordering property in a virtual knowledge flow. The basic rules are membership, atomicity and ordering preservation.
Rule 1 (Membership). A virtual knowledge node may be abstracted from either knowledge nodes or previously defined virtual knowl-edge nodes. The membership among knowlknowl-edge nodes and virtual knowledge nodes is transitive. If x is a member of y and y is a member of z, then x is a member of z.
Rule 2 (Atomicity). A virtual knowledge node is an atomic unit of knowledge access. A virtual knowledge node is activated for knowledge access if, and only if, one of its members is activated to refer knowledge. On the other hand, a virtual knowledge node has completed its knowledge access if, and only if, all of its mem-bers have completed their knowledge access.
Moreover, if an ordering relation (>) between two virtual knowledge nodes exists in a virtual knowledge flow, the implied ordering relation between the respective members of the two vir-tual knowledge nodes is ‘‘>’’ due to the atomicity rule.
Rule 3 (Ordering preservation). The implied ordering relation between two virtual knowledge nodes’ respective members must conform to the ordering relation in the base knowledge flow.
The procedure for deriving knowledge-flow views from a base knowledge flow is described as follows: Knowledge-flow modelers select some essential knowledge nodes based on team members’ knowledge-needs and/or the company’s information security con-trol rules to conceal detailed information.
Definition 14 (Essential knowledge node). An essential knowledge node is a knowledge node selected by knowledge-flow modelers for the purpose of generating a virtual knowledge node and generalizing knowledge concepts. To conceal confidential or detailed information, one or more knowledge nodes in a base knowledge flow should be selected as the essential knowledge node(s).
There are three sets of knowledge nodes: (a) The Essential Knowledge Node Set (ENS) represents the knowledge nodes se-lected by knowledge-flow modelers; (b) The Expanding Knowledge Node Set (ES) includes the knowledge nodes in ENS and the knowl-edge nodes which are added due to order-preserving property; and (c) The Neighboring Knowledge Node Set (NNS) represents the neighboring (adjacent) knowledge nodes to the knowledge nodes in ES. The knowledge nodes in NNS are candidates to be added to the ES for preserving the ordering property of a virtual knowledge node. If the implied ordering relation between any knowledge node in NNS and any knowledge node in ES does not comply with the original ordering relation in the base knowledge flow, the vio-lated knowledge nodes in NNS should be incorporated into ES. Def-inition 15 defines a minimum expanding knowledge node set (MES) to ensure that only the necessary knowledge nodes are added, thus preserving the ordering relation while expanding ES. Definition 15 (Minimum expanding knowledge node set, MES). This set includes both the essential knowledge nodes and the minimum required knowledge nodes which are added to preserve the ordering relation in virtual knowledge nodes. The implied ordering relation between any knowledge node in ES and any knowledge node not in ES must comply with the original ordering relation in the base knowledge flow. Note that an ESi(a superset of ENS) is a MES if ESisatisfies the order-preserving property, and the ESidoes not contain other ESj(a superset ENS) that also satisfies the order-preserving property.The MES only contains the essential knowl-edge nodes and the required knowlknowl-edge nodes to preserve ordering relations. Based on the MES, one can generate virtual knowledge nodes and virtual dependencies, as well as derive knowledge concepts of virtual knowledge nodes.
4.3. The procedure for discovering the minimum expanding knowledge node set
For a given knowledge flow, KF = hKNS, DSi, and an essential knowledge node set, ENS,Fig. 7shows the procedure for discover-ing the minimum expanddiscover-ing knowledge node set MES. Initially, the algorithm creates a working set ES1of the expanding knowledge node set (ES) that initially equals to the essential knowledge node set (ENS). According to the ordering preservation rule and ES defi-nition, "x 2 KNS, x R ES, the implied ordering relation between x and all members of ES must conform to the ordering relations in the knowledge flow, KF. ENS is the starting point to the discovery of MES. A while loop (lines 7–10) repeatedly finds any neighboring knowledge node that violates the ordering relation. If a neighbor-ing knowledge node violates the orderneighbor-ing relation conditions (line 9), it is added into ES. Finally, "x 2 KNS and x R ES; if the implied ordering relations between x and all of the members of ES satisfy the ordering preservation, the repeat-until loop stops at this point. The final ES is the MES, which is the knowledge node set ANS of a virtual knowledge node, vx, derived from ENS.
4.4. The procedure for discovering virtual dependencies
All virtual knowledge nodes can be derived from a knowledge flow KF = hKNS, DSi that form the VKNS of a virtual knowledge flow VKF, by repeatedly executing the procedure inFig. 7. For any pair of VKNS’s members, vx and vy, the virtual dependency vdep(vx, vy) ex-ists if dep(x, y) exex-ists in DS, where x is a member of vx and y is a member of vy.
4.5. The procedure for deriving knowledge concepts of a virtual knowledge node
After a virtual knowledge node, vx, has been derived, the knowl-edge concepts of vx should be derived.Fig. 8shows the procedure for deriving the knowledge concepts of a virtual knowledge node. Let ECS (Essential Concept Set) denote the set of knowledge con-cepts of essential knowledge nodes that need to be concealed or hidden. A minimum generalization policy is used to generalize (ceal) the concepts in ECS. For each concept c in ECS, the parent con-cept of c in the ontology is selected to form the knowledge concon-cept set (abstracted knowledge concept set, AKC) of the virtual knowl-edge node, vx. On the other hand, for some knowlknowl-edge nodes that are in MES but not in ENS, the corresponding knowledge concepts do not need to be generalized and are directly included in AKC of vx. Initially, AKC is derived from the generalization of the knowl-edge concepts in ECS. Then, AKC incorporates the knowlknowl-edge Knowledge Flow Knowledge Node Dependency Knowledge Concept Knowledge-Flow View (Virtual Knowledge Flow)
Virtual Knowledge Node Abstracted Knowledge Concept Virtual Dependency linked by consists of contains consists of linked by contains Domain Ontology identified in identified in abstracts from contains abstracts from
concepts of the knowledge nodes in MES but not in ENS. If some knowledge concepts in AKC are members of ECS or the descendant concepts of ECS’s members, they are removed from AKC due to the concept concealing requirement. The final step is to remove the implied (redundant) concepts from AKC; hence, the knowledge concepts of vx can be obtained.
If knowledge-flow modelers want to add or delete knowledge concepts in a KFV, it is appropriate to do these operations in the corresponding base knowledge flow and then re-generate a new KFV to replace the old one. A KFV is derived from a base knowledge flow. Conceptually, it is difficult to map back any changes in a KFV to the corresponding base knowledge flow. Thus, these operations should be made in the base knowledge flow. A similar concept ex-ists in database management systems, where administrators mod-ify the definition of a database view by adding or deleting fields in the underlying base tables.
Definition 16 (Implied concept). A concept c is implied under a concept set C if c can be inferred by other concepts in C. Based on a domain ontology, the concept c is mapped to an ontology node k that has n child ontology nodes ki(i = 1, . . . , n). The concept c is an implied concept if each ki’s corresponding concept is either in concept set C or can be implied by other concepts in concept set C.
5. Case illustration and analysis
This section uses a knowledge flow of a mobile phone company, named Smart-Tech Company, to illustrate the application of the knowledge-flow view. The knowledge flow represents the knowl-edge-needs that a project team requires when conducting a mobile phone development process in the company. According to the pro-cess, knowledge-flow modelers consult domain experts and team participants to acquire important knowledge-needs and identify
corresponding knowledge concepts for the purpose of representing knowledge-needs in a knowledge flow.
In this company, the mobile phone development team requires participants from various departments. Those team members have different task functions: (1) the project manager controls and coor-dinates the project, (2) the marketing analyzer conducts the busi-ness analysis, (3) the designer is responsible for product design, (4) the salesperson focuses on product commercialization, (5) the inspector carries out the quality assurance tasks, and (6) the sourc-ing planner oversees the management of outsourced parts. Based on different knowledge-needs, knowledge-flow modelers can de-sign knowledge-flow views for individual participants.
The following discussion pertains to the sourcing planners at this company whose task function is parts outsourcing. First, knowledge-flow modelers make the concealing criteria for the sourcing planners, as required by the information security policy of the company and in consideration of the information granularity suggested by domain experts. Hence, the Essential Knowledge Nodes are identified based on the concealing criteria and all knowledge concepts in the Essential Knowledge Nodes should be included in an Essential Concept Set. Then, a virtual knowledge node is obtained by the order-preserving approach to ensure that the ordering in the base knowledge flow is retained. Finally, the knowledge-flow mod-elers abstract the knowledge concepts in the Essential Concept Set using the domain ontology and the minimum generalization policy.
The knowledge flow inFig. 9includes nine knowledge nodes, k0 to k8, where each knowledge node contains multiple knowledge concepts. The knowledge concepts of k2 are too specific for the sourcing planners, so the knowledge-flow modelers make a con-cealing criterion hsourcing planner, k2, Yi to meet their knowl-edge-needs. Another concealing criterion hsourcing planner, k4, Yi is also made because the knowledge concepts of k4are confidential for the sourcing planners. Following the two concealing criteria, Fig. 7. Procedure for discovering the minimum expanding knowledge node set, MES.
the knowledge-flow modeler selects two knowledge nodes, k2and k4, as the essential knowledge nodes for the sourcing planners.
The knowledge-flow modeler applies the procedure inFig. 7to obtain a virtual knowledge node. Initially, the neighboring knowl-edge node set NNS = {k1, k3, k7} and ENS (essential knowledge node set) = ES (expanding knowledge node set) = {k2, k4}. Knowledge node k3is added into ES since the ordering of k2 is higher than the ordering of k3(i.e. k2> k3), but the ordering of k4is not higher than the ordering of k3 (i.e. k4 k3). Knowledge node k7is not added into ES since k2> k7and k4> k7. Knowledge node k1is not added into ES since k1> k2and k1> k4. Therefore, ES is changed to {k2, k3, k4}. In the second execution, NNS = {k1, k7} and ES = {k2, k3, k4}. Knowledge node k1 and knowledge node k7 are not added into ES because the implied ordering relations between each member in NNS and ES satisfy the ordering preservation rule. Therefore, the execution stops. The minimum expanding knowl-edge node set MES includes knowlknowl-edge nodes {k2, k3, k4}, and a vir-tual knowledge node vk1is derived as shown inFig. 10.
After discovering the minimum expanding knowledge node set MES, the knowledge-flow modeler uses the procedure inFig. 8and the ontology inFig. 11to derive the knowledge concepts of vk1 based on the minimum generalization policy.
Initially, the essential concept set ECS equals to {Consumer behavior, Display options, Battery options, Card options, Design rule of RF and Baseband}. After performing the Generalization function
on ECS, the knowledge-flow modeler obtains the abstracted knowl-edge concept set, AKC = {Consumer analysis, Mechanical parts, IC components}. Then, the knowledge concepts Hardware design alter-natives, Price/performance of parts and Service level agreement of third parties are added in AKC, since knowledge node k3is in MES but not in ENS. Hence, AKC = {Consumer analysis, Mechanical parts, IC components, Hardware design alternatives, Price/performance of parts, Service level agreement of third parties}. The knowledge con-cepts of vk1can thus be obtained, as shown inFig. 12.
Fig. 12 shows that vk1 has a redundant knowledge concept, Hardware design alternatives, that can be inferred by Mechanical parts and IC components.Fig. 13shows the result after removing the concept Hardware design alternatives. The final knowledge con-cepts of vk1are: Consumer analysis, Mechanical parts, IC components, Price/performance of parts and Service level agreement of third parties.
The example given demonstrates the knowledge-flow view from the sourcing planners’ perspective. If the sourcing planners are not satisfied with the view that has been generated, knowl-edge-flow modelers can repeat the same steps to abstract a new knowledge-flow view by identifying other knowledge nodes as essential knowledge nodes. Similarly, it is possible to create other knowledge-flow views from other members’ perspectives. Hence, the proposed knowledge-flow view model can enhance conven-tional knowledge flow models by supporting different team
mem-k0 Design rule of RF and Baseband 1. Consumer behavior 2. Display options 3. Battery options 4. Card options 1. Pricing strategy 2. Direct channel selection 3. Operators bundling 1. Geographic segmentation 2. Psychographic segmentation 3. Consumption environment 4. Consumer behavior 1. Multimedia functions 2. Internet access options 1. Hardware design alternatives 2. Price/performance of parts 3. Service level agreement of 3rd parties 1. Portability 2. Customized features 3. Human factors Features of iOS and Android k1 k2 k3 k4 k5 k6 k8 1. Compliance guidance 2. Usability checklist k7
Fig. 9. Knowledge flow for the sourcing planners, where nodes k2and k4are essential knowledge nodes.
vk1
1. Pricing strategy 2. Direct channel selection 3. Operators bundling 1. Geographic segmentation 2. Psychographic segmentation 3. Consumption environment 4. Consumer behavior 1. Multimedia functions 2. Internet access options 1. Consumer behavior
2. Display options 3. Battery options 4. Card options
5. Hardware design alternatives 6. Price/performance of parts 7. Service level agreement of 3rdparties 8. Design rule of RF and Baseband 1. Portability 2. Customized features 3. Human factors Features of iOS and Android k0 k1 k5 k 6 k8 1. Compliance guidance 2. Usability checklist k7
bers with various knowledge-needs. Finally, every team member can obtain a proper knowledge-flow view to support his/her knowledge-needs in the collaborative knowledge support platform.
To test the practical implications of this study, a preliminary analysis was conducted. Several professionals were invited to examine the case and related concepts to investigate whether the theoretical model could benefit them. Overall, there was general agreement regarding the feasibility of the KFV model and its prac-tical value. They thought that the KFV model could enhance typical knowledge flows to serve all team participants with their various knowledge-needs. For example, an interviewee mentioned that he would be able to communicate with a hardware designer more efficiently because the ontology of the theoretical model provided a common understanding of the general knowledge of hardware design. By referring to their different knowledge-flow views, both participants would be able to better understand their different knowledge-needs. Such understanding has the potential to im-prove the quality of communication and increase the efficiency of knowledge sharing.
The results of the preliminary analysis are summarized as fol-lows: (1) the visualized knowledge flows and knowledge-flow
views help team members to easily formulate their knowledge-needs and quickly obtain a consensus under a common domain ontology. Thus, the quality of their communication and decision making is improved; (2) knowledge-flow modelers can produce concealing criteria to protect confidential knowledge from unau-thorized access and solve the information overload problem by abstracting detailed knowledge; and (3) in organizations, knowl-edge-flow views extend the efficiency of knowledge flows and im-prove the effectiveness of knowledge sharing and knowledge support.
5.1. Implications and discussion
This study contributes to knowledge management develop-ment, first by showing how a knowledge flow can address knowl-edge-needs. In the literature, models that formally illustrate both a knowledge flow and the corresponding knowledge-needs of work-ers are lacking. The proposed knowledge-flow model in Section3
fills this gap by including three initiatives: (1) it adopts domain ontology to describe knowledge-needs by a composition of knowl-edge concepts; (2) it derives knowlknowl-edge nodes from the activities in processes to visually display workers’ knowledge-needs; and Consumer Beha vior Consumption Environment RF Ba seba nd Displa y Ca rd Rea der Ba ttery Marketing
Mobile Phone Development
Industrial Design …
Hardware Design Software Design
… Consumer Analysis Sales … IC Components Mechanical Parts Outsourcing Service Level Agreement Price/ performa nce Market Segmentation ... Quality Verification …
Fig. 11. A partial domain ontology of mobile phone development.
vk1
1. Pricing strategy 2. Direct channel selection 3. Operators bundling 1. Geographic segmentation 2. Psychographic segmentation 3. Consumption environment 4. Consumer
behavior 1. Multimedia functions
2. Internet access options
1. Consumer analysis 2. Mechanical parts
3. Hardware design alternatives 4. Price/performance of parts 5. Service level agreement of 3rd parties
6. IC components 1. Portability 2. Customized features 3. Human factors Features of iOS and Android k0 k1 k5 k 6 k8 1. Compliance guidance 2. Usability checklist k7
(3) it defines flow dependencies as the sequence of an individual’s or group members’ knowledge-needs and/or the order of referenc-ing codified documents. The knowledge-flow model helps researchers to obtain a clear view of knowledge-flow research.
Additionally, this study investigates the shortage of knowledge support in collaborative teams. The workers in a team usually have different knowledge-needs according to their task functions. For example, the mobile phone development process illustrated in Sec-tion5involves six task functions. The workers in these task func-tions need to access different knowledge concepts at different conceptual levels to conduct their work and communicate with each other. However, conventional knowledge-flow models do not provide the different views of a knowledge flow that are re-quired to address individual needs. In Section4of this study, we propose a knowledge-flow view model to meet this and related challenges. According to the proposed model, knowledge-flow modelers select some knowledge nodes from a base knowledge flow to generate virtual knowledge nodes that conceal confidential or detailed information. Through an order-preserving approach and a knowledge concept generalization mechanism, a knowl-edge-flow view is generated. The proposed innovative model al-lows various knowledge-flow views to be generated that meet the individual knowledge-needs of different workers. These knowl-edge-flow views not only comply with organizational information security policy but also reflect the granularity of knowledge-needs. Thus, this study advances the conceptual applicability of
knowl-edge flow research to cooperative knowledge support
environments.
Practical implications can be derived from both the proposed knowledge-flow model and knowledge-flow view model. The knowledge-flow model can help organizations assess their current practices of knowledge sharing and knowledge reuse to gain in-sights into required knowledge concepts and build appropriate knowledge flows. The knowledge-flow view model can facilitate knowledge support in cooperative teams to improve team produc-tivity and communication quality. Moreover, both models can be applied to any knowledge-based organization where business pro-cesses are conducted by cooperative teams in a dynamic working environment.
6. Conclusion and future work
In knowledge-intensive working environments, workers need task-relevant knowledge and documents to support task perfor-mance. To meet these requirements, many organizations have built knowledge support platforms that allow workers to preserve, share and reuse task-relevant knowledge. The value of knowledge support thus pertains to the importance of realizing knowledge
management and promoting business intelligence in knowledge-based organizations.
Knowledge flow models have been proposed as an effective tool for building knowledge support platforms, and in recent years, a number of studies have focused on knowledge flow models and applications in business and scientific research contexts. Knowl-edge flows represent the flows of an individual’s or group mem-bers’ knowledge-needs and the referencing sequence of documents in conducting business operations and/or research activities. By depending on knowledge flows, organizations can facilitate their knowledge support mechanism by providing codi-fied documents to workers that fulfill their knowledge-needs for individual tasks. However, since these task functions vary in a col-laborative environment, different knowledge flow views are re-quired. Conventional knowledge-flow models do not provide different views of a knowledge flow; this decreases the efficiency of knowledge sharing in organizations that depend on these mod-els. To satisfy team workers with different knowledge-needs, this study proposes the knowledge-flow view model, which is capable of generating multiple knowledge-flow views.
The KFV model builds knowledge-flow views by abstracting knowledge nodes in a base knowledge flow to generate corre-sponding virtual knowledge nodes through an order-preserving approach and a knowledge concept generalization mechanism. The knowledge-flow views not only fulfill workers’ different knowledge-needs but also facilitate knowledge support in team-work. In summary, the KFV model advances the conceptual appli-cability of knowledge flow research to cooperative knowledge support environments and helps researchers to obtain a clear view of knowledge-flow research. It also improves the effectiveness of knowledge sharing and knowledge support in organizations. 6.1. Limitations and future work
One limitation of this work is the lack of a rigorous evaluation of the KFV model’s practical benefits. Because this study constitutes fundamental knowledge flow research, it aimed to generate knowl-edge-flow views and extend knowledge flow research to coopera-tive teams by establishing a KFV model with novel methodology. The KFV model could be the core for building KFV systems based on the theoretical contributions achieved in this work. In the fu-ture, we will build a KFV system to realize the practical benefits of the KFV model and its related methodologies. An empirical study will also be conducted to quantify user satisfaction and business values by questionnaires or other measurement tools.
Another limitation of this study is that it does not consider how to integrate a workflow model. Organizations often adopt work-flow models to manage the information in the business processes
vk1
1. Pricing strategy 2. Direct channel selection 3. Operators bundling 1. Geographic segmentation 2. Psychographic segmentation 3. Consumption environment 4. Consumer
behavior 1. Multimedia functions
2. Internet access options 1. Consumer analysis
2. Mechanical parts 3. IC components 4. Price/performance of parts 5. Service level agreement of 3rd parties 1. Portability 2. Customized features 3. Human factors Features of iOS and Android k0 k1 k5 k 6 k8 1. Compliance guidance 2. Usability checklist k7
