Examining the Representation Model of Managers in Strategic Foresight Using Raugh Set theory

Subject Areas : Strategic Management Researches

Mohammad Ehsan Souri ¹ , Morteza Soltanee ² , bahman hajipour ³ , Hamidreza Yazdani ⁴

1 - PhD student of Business Administration, Faculty of Management and Accounting, Farabi Faculties, University of Tehran, Qom, Iran
2 - Associate Professor in Business Administration, Faculty of Management and Accounting, Farabi Faculties, University of Tehran, Qom, Iran
3 - Associate Professor in Business Administration, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran
4 - Assistant Professor in Business Administration, Faculty of Management and Accounting, Farabi Faculties, University of Tehran, Qom, Iran

Received: 2022-11-06 Accepted : 2023-01-25 Published : 2023-02-20

Keywords: representation, Strategic Foresight, strategic decision-making, Managerial Cognition, Raugh Set Theory,

Abstract :

Strategic foresight is the central core of many strategic management theories and increases the possibility of success of strategies in the four environments of volatility, uncertainty, complexity and ambiguity (VUCA). This study hinges on the complexity of mental representation. According to research literature, the degree of complexity of representation is contingent due to different contexts but it has not been investigated before. In this study, we extend the past literature to determine the patterns governing the relationship between representational complexity and strategic foresight in four different contexts (VUCA) using the Raugh set theory.However, the theoretical gap in the research literature is focused on what level of complexity of representation in what context of the four environments creates an acceptable strategic foresight. we have used the Raugh set theory to discover the patterns of the representational complexity achieving desired strategic foresight in different VUCA environments. The input of Raugh set theory includes two general types of decision and conditional variables. The conditional variables include "representational complexity" and "environmental context," and the decision variable also encloses "strategic foresight." The results show that when the future is difficult to predict (environments with uncertainty and ambiguity), simple and less complex representations create more useful strategic foresight. At the same time, when it is easy to predict the results of an action (complex and volatile environments), whether understanding the current situation is simple or intricate, using complex representations is more effective

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