1. Introduction
Knowledge has become the most valuable resource of organizations, which is considered as the main source of competitive advantage [1,2]. Knowledge management can be seen as a means to increase the competitiveness of organizations through the creation, dissemination, sharing and use of knowledge [3].
Considering the knowledge management is essential for the competitiveness and sustainability of organizations, the challenge is to develop mechanisms that enable the creation, dissemination and use of knowledge. However, the creation and/or acquisition of knowledge by organizations is not easy [4] and its dissemination between individuals, organizations and networks is even more complex [5-7].
It’s possible to identify at least two patterns of organizational knowledge dissemination. The first refers that knowledge flows are geographically located [8] and the other one assumes that knowledge is diffused more easily within than among organizations [7,9].
Knowledge can be extremely difficult to be transmitted across regional boundaries (geographical) in some cases, but, in the other hand could be relatively close among organizations [4]. In this way, to understand the knowledge flow in a supply chain can facilitate the creation, modification, dissemination, and application of knowledge in this context. This is evident in processes related to the dissemination of knowledge, which can be understood by modeling the knowledge flow between the organizations involved.
It is important to consider that the knowledge flow exists at individual, group and organizational levels. The mechanisms involved in the flow between organizations can help to improve solutions and minimize problems associated with processes within supply chains.
This paper presents some ways to analyze knowledge flows between organizations. A literature review was conducted discussing the main issues addressed in the research up until 2010. Finally, we illustrated how the theory of these cases and analogies can be applied in a production chain to reduce bottlenecks and problems.
2. Theoretical Background
2.1. Knowledge Flow
Knowledge flow is defined as the process of knowledge “movement” from a source to a receptor and its subsequent absorption and utilization, in order to improve the organization’s ability to perform the activities [3]. This can be understood as a process of knowledge passing between people or knowledge processing mechanisms [10].
According to Zhuge [3], the points of issuing/the receiver of knowledge is known as a “knowledge node”. The flow has three key attributes: direction, content and a carrier, which respectively determine who sends and who receives, the knowledge content, and the way in which the content is transmitted. A knowledge node can be “a team member” a “role”, or a knowledge portal or process [10].
Yoo, Suh and Kim [11], pointed out that knowledge flow in organizations can identify problems in the corresponding business processes. Clear identification and optimization of the knowledge flow can ensure the effective use of knowledge within the organization, enhancing the dynamic between organizational knowledge and business processes.
Mapping and identifying the knowledge flow are strategically important to organizations in three main aspects [11]:
1) The flow of knowledge transmits the know-how generated in a sub unit to other locations within the organization;
2) Knowledge flows facilitate the coordination of workflows linking several sub units which are geographically dispersed;
3) Knowledge flows enable organizations to capitalize business opportunities that require collaboration of several subunits.
The advantages and importance of organizations to understand how knowledge flows within their structures is clear. It becomes crucial in understanding the characteristics of these flows and their representative forms.
2.2. Knowledge Flow Characteristics
The fundamental characteristics that constitute a knowledge flow are [3]:
1) Accumulation of information: enables the accumulation of information and or knowledge during task execution. It also allows to keep the knowledge for later use;
2) Classification: ability to classify knowledge according to different projects and different work teams;
3) Abstraction: the ability to reflect about the knowledge at different levels of abstraction and refine its content;
4) Analogy: should establish similar associations between related content;
5) Management of the version: shall support the management of the evolutionary process of knowledge flow through addition, union and/or disposal of the flows.
Zhuge [3] indicates four basic types of knowledge flow (Figure 1):
1) Sequence Connection: knowledge comes from two streams and forms one unique stream;
2) Junction connection: two or more streams converging in one flow only;
3) Division of flows: the flow can be divided into two or more flows of knowledge;
4) Dissemination of the flows: the flow can be distributed into multiple streams of knowledge.
Yoo, Suh and Kim [11] proposed ten guidelines in order to diagnose and redesign the flow of knowledge processes. These guidelines aim to organize the flow of knowledge maps, aligning it with the processes of an existing workflow. According to this set of guidelines, is argued that the knowledge flow must be linear, clear and objective, avoiding parallel flows of knowledge (unnecessary flows) within the same check point and/or business process. Moreover, it is important to consider the objectives of these businesses and for its sub-processes and tasks.
The model proposed by [11] aims to redesign business processes by mapping the knowledge flow. According to the authors, we can infer that the redesign of processes based on knowledge flow reduces costs and time need for tasks, maintain the quality standard and makes business processes more flexible.
Zhuge [3] and Yoo, Suh and Kim [11] did some works on knowledge flows from business processes within organizations. However, the findings at the moment are not enough about how the knowledge flows can be identified and mapped in the inter-firms relationship context.
Figure 1. Representation of the knowledge flows. Source: Adapted from Zhuge [3]; *KN: Knowledge; Node/KF: Knowledge Flow.
3. Research Process
This study was conducted in two research phases: 1) Searching the literature; 2) Mapping inter-firms knowledge flows, as described below.
3.1. Phase 1: Searching the Literature
The first phase of the research was a systematic search of literature, according to the following steps: a) identification of the subject of the study (by selecting and setting the keywords and search terms); b) identification and selection of database; c) search in the database according the criteria for selection of publications; and d) analysis of the publications in the area, in order to identify research knowledge gaps. Next, the steps will be presented in a detailed way.
1) The first step was an identification of the subject of this study (by setting the keywords and search criteria). As a result of the findings, it was identified that the issue “knowledge flow” has been associated with knowledge management. In order to maximize the possibility of including the full range of relevant publications on the subject, we used this keyword and its derivatives.
2) The second step was composed by the identification and the choice of a data base. We chose the Scopus database because of its academic recognition of being one of the largest databases of abstracts and citations of peerreviewed scientific literature, which includes smart tools to track, analyze and visualize scientific production [12].
3) After that, we did a literature search in the database. We looked for “knowledge flow*” in the fields “titles, abstracts, and keywords” publications. We considered the period of September of 2010 until the oldest available database search: which was in 1960.
The criteria used was those inserted in the fields “article” or “review” or “article in press” indexed in the “areas” “Business, Management and Accounting” and “Computer Science”. The result of this search was a sample of 341 publications.
4) The last step of the first phase was organized by doing the profile analysis of published data. The 341 papers were used as the basis for performing the following analyses: number of articles published per year, the most relevant papers on the knowledge flow field (identified from the number of citations) and the main theoretical approaches of these works and gaps for the research. These analyzes will be presented in the results section of this paper.
3.2. Phase 2: Mapping Inter-Firms Knowledge Flows
The illustration of flows was based on the model proposed by Zhuge [3]. The processes mapped were obtained through a review of studies concerning the swine production chain in Brazil [13,14]. This supply chain was chosen because it contained some problems related to environmental aspects (could be reflected in the market performance). We believe that a full understanding of knowledge flows will assist in tackling and dealing with the gaps and achieve new potential markets that are currently blocked due to current problems.
4. Results
4.1. Profile of the Literature on Knowledge Flows
Previous studies have summarize the literature from different fields and have pointed a growing interest in issues related to Knowledge Management, such as intellectual capital [15], organizational memory [16], among others. We have identified that the publications on knowledge flow is also growing (Figure 2). Similar to what happens in other fields of research (e.g., organizational memory), studies on knowledge flows began with a technological approach focused on the organizational level of analysis. The emphasis of these studies is usually in the application of IT and knowledge engineering (e.g. knowledgebased systems, expert systems) to support knowledge flows within organizations.
It was observed that the term “knowledge flow” appears in the research for the first time in 1990, in a paper entitled “Toward Implementation of successful knowledge-based systems: expert systems versus knowledge sharing systems” of Kiyoshi Niwa. The paper points out the crucial role of the knowledge flow (from the knowledge suppliers through knowledge-based systems-KBS).
In order to understand the approaches of these studies about knowledge flows, we identified the twenty most relevant papers from the number of citations in the Scopus database. These 20 documents were included in our high impact papers group.
Figure 3 shows the bibliometric representation of