An Intelligent Approach to Assess Tacit Knowledge Fitness in Networked Enterprises

An Intelligent Approach to Assess Tacit Knowledge Fitness in Networked Enterprises

Khalid A. Al-Mutawah (University of Bahrain, Bahrain)
DOI: 10.4018/978-1-4666-2791-8.ch003


Many organizations attempt to form strategic networked enterprises, yet such strategies are difficult to implement because they are as likely to fail as to succeed. This failure is due to intangible differences and mismatches between partners in tacit knowledge (TK). Despite the various proposed partnership assessment models/tools in the literature, an immediate need exists for a new approach to measure the mismatch in TK across different organizations. This is due to the complex, vague, and uncertain nature of TK attributes. Hence, an instrument for measuring vagueness (imprecise), such as fuzzy linguistic variables, is needed. In this study, the author applies a neuro-fuzzy approach to assess TK fitness in networked enterprises. The results show how differences in TK between partners affect the networked enterprise’s performance. Furthermore, the assessment approach reveals the most significant values to adopt and the irrelevant values that must be abandoned to smooth the partnership formation. The proposed model can prevent unexpected conflicts between partners if managed properly.
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1. Introduction

Despite becoming a major source of challenges as well as opportunities for many industries, the globalization of the marketplace results Networked-Enterprises (NE) facing more and more global issues that are critical for their success, in particular tacit values conflicts and clashes. For example, the attempts to integrate European organizations to the global Japanese automotive organizations have resulted on pitfall conflicts because of the differences on tacit values between the western management and the Japanese management practices (Smagalla, 2004).

Therefore, assessing tacit values compatibility between partners is a critical and strategic success factor for NE. Nevertheless, current partnerships assessment attributes have included either quantitative indices, such as annual productivity and financial stability (Lorange et al., 1992), or qualitative indices, such as trademark reputation and communication openness (Talluri et al., 1999; Ip et al., 2003). Yet, sometimes evaluation attributes also include subjective indices, such shareholder’s favourability, and objective indices, such as corporate image and geographic coverage (Donaldson, 1994; Choi & Hartley 1996; Dacin et al., 1997; Mikhailov, 2002). However, these partnerships assessment models have mainly based on explicit knowledge (EK). EK is disseminated across organizations using physical objects like documents, standard operating procedures, and manual of best practices. This type of knowledge depends on facts, figures, data, and formulas to deliver its contents (Harrison, 1987). EK can be information that are used during the negotiation, coordination and communication processes which are associated with selling price of an item, cost, order quantity, and other tangible data.

On the other hand, tacit knowledge (TK), can be a supplement source of knowledge to optimize partnership assessment process. TK is intangible knowledge that is hidden inside people and represents their individual experiences, culture, beliefs and values (Nonaka & Takeuchi, 1995). Therefore, TK is often learned independently of direct instructions, because this type of knowledge is difficult and sometime impossible to be documented, transferred and shared (Nonaka, 2007).

The main contribution of this research, therefore, is proposing a TK-based model to assess NE performance. The proposed model makes use of the Adaptive Network Based Fuzzy Inference System (ANFIS) that implements a Sugeno fuzzy inference system (Jang et al., 1997).

To construct and validate the model, I used a data set of three organizations from the petrochemical industry where organizations’ tacit values (i.e., risk and feedback) are used as inputs to the ANFIS model and the anticipated partnership performance is the output. Furthermore, I applied a resampling method to re-generate an adequate sample for training the proposed ANFIS model. To present this model the paper is divided into seven parts: Section 2 provides a background for the study and definition important terms, Section 3 depicts the instrument used to model TK in this study. Section 4 describes the design of the ANFIS model, Section 5 describes the data used in this study and explains the design of the experiments, Section 6 illustrate the results of the experiments conducted on a petrochemical SC, Section 7 concludes and summarizes the study.

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