In today’s information age, companies are faced with high competition and pressure while trying to perform successfully in the long run. In order to meet the dynamic competitive environment and the globalization of accessible markets, companies have to pursue competitive power arising from the efficient use of general and private information. Accordingly, knowledge becomes a competitive factor and provides a fundamental reason for company success. Therefore, it is important not only to consider explicitly accessible information, but also to directly perform the performance-related use of a company’s specific, implicit information.
These individual experiences and knowledge, such as the basic components of human capital, include the chance to improve the direction and control processes of performance production and thus successfully manage competition. In such a performance management (PM) context, expert knowledge necessarily focuses on the relationships between causes and effects to create financial performance. Considering the cause-effect relationships underlying the financial performance creation process, the traditional perspective of value realization measurement is causally extended to ambitious value creation management. As a result, such causal information reveals options for actions affecting the financial and non-financial dimensions associated with future financial performance.
Thus, the PM provides relevant starting points to control the financial performance building process. Expert knowledge focuses on the relationships between causes and consequences inevitably to build financial performance. In reality, companies are made up of many departments with multiple environmental factors, and as a result, there are many hidden or explicit, interdependently structured features associated with performance production. Without knowing about such relationships, management cannot efficiently control the desired effects according to their cause. A map of causal relationships can provide more transparency in this regard. However, expert knowledge of success factors and their causal relationships is generally not available in a clear graphic representation.
Rather, subjective based knowledge, called implicit or implicit knowledge, originating from individuals’ observations and experiences, may be identifiable and waiting to be revealed. Knowledge management, recognized as a sub-discipline of Project Management, can be applied to transform this implicit knowledge into explicit subjective knowledge about causal relationships. Moreover, this can be defined and described by creating a specially crafted causal map. A subjective judgment bias can arise through the establishment of causal links during the mapping process. With regard to this problem of subjectivity, certain methods of the multi-criteria research field, in particular the decision-making path and assessment laboratory (DEMATEL), can be used in the mapping context. DEMATEL ensures that possible personal bias is reduced when applying one of the common mapping methods.
What is the DEMATEL Method?
Between 1972 and 1976 Fontela and Gabus developed the DEMATEL approach to structure and solve multi-criteria problems in a multi-personality context. DEMATEL represents an algebraic analysis method that brings together individual implicit information gathered to identify and measure causal interdependencies between identified success factors. Moreover, certain success factors structure rigidly according to their suitability in creating performance. The causal relationships of performance-related success factors are shown in an appropriate causal map defined as an impact relationship map. This method for creating graphics allows the problems that occur with visualization to be analyzed and solved. It is one of the methods used for the healthy conclusion and solution of especially complex and unsolvable problems.
Thanks to this method, cause-effect relationships of all factors that cause a problem can be determined and analyzed more easily. Thanks to the DEMATEL method, it is possible to visualize the complex cause-effect relationship during factor performance creation, but it may be very difficult to quantify the interaction of the resulting factors. Due to this difficulty, the DEMATEL method for determining the level of interaction is created from 7 steps to expand it into a cloudy environment. These stages are as follows:
• Determining the factors and creating the fuzzy scale,
• Creation of fuzzy direct relationship matrix,
• Creation of normalized fuzzy direct relationship matrix,
• Creation of fuzzy-sum relationship matrix,
• Determining the influencing and affecting factors,
• Determination of factor weights,
In the final step of the DEMATEL approach, a causal impact relationship map is depicted in the identified success factors and their performance-related relationships. Moreover, factors can be classified as causes and buyers. All identified success factors and only their causal relationships related to performance are visualized in an impact relationship map. This causal map is framed as a sort of coordinate system in which the abscissa represent the values of full effects and the ordinate axis is scaled to the net effect values.
Consequently, causal knowledge of the success factors underlying financial performance production is an important prerequisite for effective preventive care. For this purpose, it is necessary to benefit from the subjective experiences and knowledge of the important parts of the project manager and the employees. It is the current task of knowledge management to extract relevant tacit knowledge and make it explicitly available for the management of an organization. Subsequently, the tacit information generated by the application of an appropriate mapping method should be gathered, structured and systematized in a more general way and in a feasible way for employees. In addition, the complex process of building financial performance must be represented and analyzed in terms of their relevance to performance and their causal relationships.
The DEMATEL method shows approaches on how to construct a causal map on the basis of externalized tacit knowledge. Both methods differ in procedures and results. Causal mapping ensures low quality of defined causal structures of success factors due to the lack of quantitative evaluation and highly subjective aggregation of implicit information. However, when DEMATEL is applied in the context of mapping, subjective bias can be minimized by a systematic and transparent binary assessment of success factors. It provides intersubjectivity due to its repeatability. However, since the causal relationships discovered between factors can only be interpreted on an ordered scale, strategic predictions of future performance developments are only possible to a limited extent.
In order to reach an objective validity, the availability of sufficient data and the use of properly selected statistical procedures are required. If time series data are available for all variables of the causal map, verification of causal relationships can be done using a multivariate time series model. The variables of this map are not directly observable. However, when functionalized as latent variables with appropriate factors, structural equation modeling can be used to verify cause-effect relationships between success factors. Statistical verification of the causal relationship network objectifies previous ordered data in metric forms to achieve relative comparability and clear predictability. Thus, the importance of the map has been optimized compared to that made by DEMATEL. Finally, the realization and production of performance can be represented and analyzed qualitatively and quantitatively in an understandable way with the validated map. As a result, due to the advantages and capabilities of the DEMATEL method, DEMATEL’s approach has attracted great attention in the last decade, and many researchers apply it to solve complex system problems in various fields. Additionally, as many real world systems contain imprecise and ambiguous information, DEMATEL has been expanded for better decision making in different environments. However, within the information, no systematic review has been made for the DEMATEL technique and its applications. Therefore, a comprehensive review of the latest literature on decision-making approaches based on DEMATEL should be undertaken.
Author: Ozlem Guvenc Agaoglu