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Cognitive Approach Using SFL Theory in Capturing Tacit Knowledge in Business Intelligence



The complexity of Business Intelligence (BI) processes need to be explored in order to ensure the BI system properly treats the tacit knowledge as part of the data source in the BI framework. Therefore, a new approach to handling tacit knowledge in the BI system still needs to be developed. The library is an ideal place to gather tacit knowledge. It is a place full of explicit knowledge stored in various bookshelves. Nevertheless, tacit knowledge is very abundant in the head of the librarians. The explicit knowledge they gained from education in the field of libraries and information was not sufficient to deal with a complex and contextual work environment. The complexity comes from many interconnected affairs that connect librarians with the surrounding environment such as supra-organizations, employees, the physical environment, and library users. This knowledge is contextual because there are various types of libraries and there are different types of library users who demand different management. Since tacit knowledge hard to capture, we need to use all possible sources of externalization of tacit knowledge. The effort to capture this knowledge is done through a social process where the transfer of knowledge takes place from an expert to an interviewer. For this reason, it is important for the interview process to be based on the SFL theory (Systemic Functional Linguistics).

The cognitive approach is ideally suited for capturing knowledge as from among the massive data available these days. The decision-maker typically must integrate multiple streams of information from the information or other collaboration with the knowledge systems in making decisions [1]. Furthermore, decisions may be based on organizational politics or routines [2], and decision-makers may limit themselves to a few choices because of “bounded rationality” [3]. Ducharme and Angelelli [4] invented the use of cognitive as advanced analytics to capture and extract tacit knowledge by elaborating the predictive analytics, stochastic analytics, and cognitive computing. Moreover, the advanced analytics approach still is implemented in the Business Intelligence (BI) environment [17]. Thus, the basic BI framework involving a tacit knowledge approach can be illustrated as shown in Figure 1.

There is a small number of earlier research about business intelligence in the academic library and library profession. An example of this research is Cox and Janti [5] on the Library Cube project, a business intelligence system that demonstrates the value that can be provided by academic libraries. However, the research is not targeting the tacit knowledge at all since it is only targeting the provided information in the academic information system. Heims et al [6] mention that reporting BI research and creating BI reports are the key area of responsibility of librarians in the information era. We addressed the problem by open dialog with the librarian, which actually what considered would happen between BI manager and librarian to develop clear communication channels [7]. Noted that for librarians, BI is part of their challenge in the information era [8].
Since tacit knowledge is hard to capture, we need to use all possible sources of externalization of tacit knowledge [9]. The effort to capture this knowledge is done through a social process where the transfer of knowledge takes place from an expert to an interviewer. For this reason, it is important for the interview process to be based on the SFL theory. 

According to SFL theory, only a fraction of “can-do” turned into “can mean” and only a fraction of “can mean” turned into “can say” [9]. This is what is meant by Polanyi when he said “we know more than we can tell” [10]. Hence, only a portion of tacit knowledge can be captured by linguistic means. We need other means that came up from “can mean” which anything that could analyze semiotically. It could be non-verbal cues or drawing, written text, etc.  We refer to drawing, photograph, videos, written text, and others as the documented sources and beyond our analysis. Here we just focused on non-verbal cues. However, whenever documented sources considered relevant, we could use it as a source of tacit knowledge.

A. Linguistic Source of Tacit
According to SFL theory, language is realized in four strata: semantic, lexicogrammar, phonology, and phonetics [11]. Semantics is the highest level that explains the hidden meaning of language. Lexicogrammar is an aspect of language that explains the real meaning, can be seen from the choice of words and grammar used. Phonology is the meaning that exists in sound. Phonetics is speech that arises from language activities. It can be seen that this stratification moves from something abstract (semantic) to something concrete (phonetic).
Someone will choose a word to represent his experience when speaking. What word or wording was chosen can distinguish whether the experience or knowledge expressed is an inheritance or not. In fact, sometimes, a person will find it difficult to find the right words to describe their knowledge so that they choose.

Even after knowledge has been expressed verbally and non-verbally, there is still space where the knowledge of tacit cannot be expressed at all and can only be demonstrated by behavior. Apart from observations requiring precise and specific time, experts generally do not like being observed while working [12]. In addition, observations become more complicated when several experts are involved [12]. This can only be done in a non-intrusive manner such as a surveillance camera, but it can be a problem with privacy issues. Alternatively, observations can be made through third-person testimonies. In this case, the interview was conducted on the third person who had witnessed the behavior of the first person who was the target to reveal the knowledge of his possessions.

The framework above shows the design used to capture the comprehensive knowledge of experts. Based on the SFL theory, tacit knowledge consists of three levels. The first level is the most basic level where a person can only do but cannot interpret it, let alone say it. This knowledge is contextual tacit knowledge because it can only be raised in a supportive context. It can only be collected through observation. Even so, because the context is very specific, in terms of space and time, only people present in that context can see and understand from their perspective what the tacit knowledge is. In this study, it is assumed that the person is a peer. Researchers collected data on tacit knowledge from peers through cognitive interviews. Furthermore, we can conclude there are two ways to collect tacit knowledge:
1. Focused on the stated problem. Participant presented with a problem which needs tacit knowledge to be solved. The tacit knowledge needed to solve this problem can be collected with interviews, based on respondents chosen with questionnaires. Questions in the interview informed by problems urgency, detected by questionnaire. Here, sequences of the steps determine the completeness of tacit knowledge. The figure below show the connection between questionnaire design and decision.