مدیریت مبتنی بر سناریوی خلاقیت فردی

When we consider the fiercely competitive environment in which modern companies operate, highly creative people can be considered strategic assets in furthering companies’ competitiveness. This research provided a novel approach to creativity management through scenario analyses that applied the Bayesian network. This article focused particularly on perceptions of individual creativity and asked two questions: how do the processes of creative revelation—exploitation and exploration—contribute to building individual creativity, and how do environmental factors—task complexity, and bureaucratic and supportive cultures—affect individual creativity? The Bayesian network seems appropriate from this perspective because Bayesian network structure addresses the causal relationships between all variables. For the empirical test, we collected questionnaires and applied the Bayesian network to the survey data to extract a set of reliable causal relationships. By performing scenario-based simulations—both “what-if” and goal-seeking simulations—we found that individual creativity can be managed very effectively by adjusting the related variables in such a way as to maximize that quality.

For decades, companies have used many conventional management resources to build and exercise strategies more effectively in competitive markets. A key problem with this approach is that few tangible resources remain for companies to exploit further; intangible resources need to be found and applied to enhance the effectiveness of management. In this respect, creativity management has emerged as an important strategy. As a result, a number of people have insisted that it is necessary to strengthen employees’ creative outcomes in order for an organization to achieve a competitive advantage. However, existing studies on creativity have been limited to discussing academic issues, most of which practitioners cannot adopt realistically. To overcome this, we employed scenario-based simulations applying the Bayesian network (BN) to provide a novel approach to the management of individual creativity. Companies experience their greatest difficulty when faced with an uncertain management environment; scenario planning can be used to mitigate risk in such circumstances. Since the global financial crisis, scenario planning as a tool to analyze and forecast uncertain circumstances has attracted attention as a strategic method to prepare for unfavorable business environments. In fact, scenario planning originated from army war games. It was used then as a method to establish strategy within the organization and also as a strong tool for organizational learning and change management. A scenario deals with two worlds comprised of facts and perceptions, and it may be said that even though it explores the facts, it targets perceptual systems in the decision making process. It assists in redirecting an unbalanced perception by making one realize where reality leads without becoming preoccupied with contemporary stereotypes and beliefs. Therefore, through several scenarios, this study analyzed how to manage changes in the relationship between individual creativity and the various elements that affect it. It is widely known that a variety of variables may affect creativity, ranging from organizational culture and leadership to individual level of knowledge. Over the past decade, research on creativity has proliferated (Shalley, Zhou, & Oldham, 2004). However, in order for practitioners to apply academic findings to the real issues of creativity management, extraction of causal relationships from among the set of variables relevant to creativity is necessary. This research focused on perceptions of individual creativity and asked two questions: how do the processes of creative revelation—exploitation and exploration—contribute to building individual creativity, and how do environmental factors—task complexity, and bureaucratic and supportive cultures—affect individual creativity? To investigate these questions, we adopted the methodology of the BN. The BN, also called the Bayesian belief network, is growing in popularity as a probabilistic modeling method to describe uncertain knowledge and causality (Daly, Shen, & Aitken, 2011). The useful feature of the BN is that it not only constructs a causal network among latent variables, but also helps in conducting comprehensive scenario-based simulations. Further, it has received attention as a complementary technique for structural equation modeling (SEM) in exploring causality from empirical data (Zheng & Pavlou, 2010). This is because SEM methods have three main limitations: lack of causality inference, restrictive model structure, and lack of nonlinearities (Lee, Barua, & Whinston, 1997). Thus, we investigated causal structures among variables that have direct and indirect effects on individual creativity and then conducted scenario-based simulations based on those structures. Research on the BN began with the naïve Bayesian network (NBN), which in simple form, was highly accurate in classification issues (Langley & Holcomb, 1992). However, the NBN considers a class node (or dependent variable) as the special variable that differs from other nodes, while class node is also considered as one of the ordinary nodes in the general Bayesian network (GBN). That is, in the GBN, unlike other BN classifiers, even class node expresses the interdependency among all nodes as one BN, without distinguishing it from other nodes (Bouckaert, 1995). The GBN’s strength lies in its ability to express the probabilistic causality (or interdependency) that exists among many variables that belong to a decision making problem. Therefore, the GBN was used in this research to consider creativity and the complexity among various elements that affect it. The GBN has been applied successfully to the resolution of highly complicated decision making problems (Cheng and Greiner, 2001 and Madden, 2009). With the GBN, we can simulate and experiment with variables using such varied techniques as “what-if” and goal-seeking analyses. By applying the GBN to survey data, we were successful in extracting causal relationships among exploration, exploitation, and other relevant factors affecting individual creativity. In the social dimension of creativity, research has found that different social effects and environments exercise different influences on individuals. Thus, we must take social variables into account when we consider individual creativity. In this context, Ryhammar and Smith (1999) considered organizational structure, culture, and work pressures to be important factors influencing creativity. They also tried to determine the relationship between personal attributes and environment. Therefore, this research also considered the influences of environmental factors on creativity. Empirical findings revealed that by taking advantage of the flexible structure and inference capabilities supported by the BN, a balance between exploration and exploitation can be obtained effectively by adjusting related factors such as task complexity, and bureaucratic and supportive cultures.