BUS602: Marketing Management

Our methodology is based on developing an intelligent large scale participators approach for the environmental scanning process. It provides the distributed interaction capabilities and helps for building and managing knowledge repositories for the environmental scanning process. Different knowledge-based and model-based methods are integrated to achieve this task. In addition, we enhanced the web-based RT-Delphi model by adding visualization and explanation capabilities and integrating with an ontology KB model. The widely used environmental scanning models that are SWOT, PESTEL, and MICMAC analysis, also, are integrated with the enhanced RT-Delphi to perform the environmental scanning task. 

As shown in figure.1, the developed framework consists of five main components, which are model-base, knowledge-base, model-based management system, knowledge-based management system, and graphical user interface components. There are different models in a knowledge-base system, which are enhanced RT-Delphi, ontology building models, "What if" and "Why" Explanation. Also, SWOT, PESTEL and Structural Analysis are the three models that represent the model base components. 

The model-based and knowledge-based management systems components provide the integration and execution of all models. Finally, a graphical user interface sub-system provides the policy/decision maker capabilities for reporting consensus summary information, explanation, and visualization capabilities. In the following, the methodology of the IESA will be illustrated in each of the previous flowcharts: 

The enhanced RT-Delphi model is used as a knowledge acquisition tool for ontology building. It cooperates with an ontology building editor, as shown in figure 2, to generate a domain ontology KB. The developed ontology KB consists of four sub-ontologies: model drivers, model variables, participators, questionnaires which consist of different concepts. Moreover, each sub-ontology consists of different concepts' prosperities (name, description, weight ranking, and its impact). 

Figure 3 explains the identification phase. It plays a fundamental role in environmental scanning. It is used to identify all drivers by utilizing experts' knowledge and their imaginations. This phase provides to identify the external drivers and current internal strength and weakness. In this phase, we enhanced the web-based RTDelphi model by adding visualization and explanation capabilities. Also, the widely used environmental scanning models that are SWOT, PESTEL are integrated with the enhanced RT-Delphi to perform the environmental scanning task. Figure 4 shows the integrated ERT-Delphi with SWOT and PESTEL analysis. 

Figure 6 explains the structural analysis phase. Based on integrating two futures studies methods, which are MICMAC and the enhanced RT-Delphi (ERT-Delphi) method, MICMAC E-RT-Delphi will be used to identify the major drivers, which are essential to the system's development. As shown in figure7, the knowledge acquisition process of structural analysis is based on the RT-Delphi numerical questionnaires. The knowledge acquisition screens are classified as two types: the first is a guide-knowledge that contains for each question median response of the domain experts group, the number of responses made, justifications that others have given for their responses, and finally, the related links for the questionnaires' types. Also, the second type is judgment - knowledge that allows the domain experts to add new respondent's numerical answers and type their justifications for their own answer(s). A large-scale of the domain experts can fill in the MICMAC E-RT-Delphi matrix over a specific period of time determined by the domain analysts. When the relationship between domain drivers is direct influence, the filling-in direct influence is low (1), medium (2), or high (3). In addition, zero value (0), appears if there is not a relation. 

After the knowledge acquisition process is finished, the system runs MICMAC algorithm raising the structural analysis matrix to the power of successive values (power seven is widely used in literature) to find the indirect relations between each pair of drivers. After that, the final matrix is normalized, and the summation process for both rows and columns are applied. The visualization and report generation components are used for generating a consensus report for policy/decision makers.