In many practical situations, technical decision making, and policy recommendations critically depend on the outcomes of formulated and simulated statistical models. Selection of model variables is the stepping-stone to ensure its reflection of real-world behaviours. However, modelers usually rely on available data, experience, general knowledge, and review of the literature when selecting model variables, without giving due consideration to the very distinct context of the community whose behaviour is being analysed. Community’s input, if at all, is only gathered on the already defined model variables and using various techniques, e.g., significance of regression test, statistical analysis performed on the “restricted” data is used for decision making and policy formulation. Although such analysis can determine if a certain pre-defined variable on which data is collected should be excluded from the list of model variables, no additional, community-particular variables can be included in the list, simply because no data was collected on these variables. Therefore, key details about a community that must be considered may be left unaccounted. This work emphasizes the importance of community engagement at every step of model formulation. Specifically, the significance of determining the context of the community under consideration and utilizing the identified context to select community specific model variables is highlighted. Using as an example, electric vehicle adoption model for different communities, this presentation will underline the importance of understanding the needs, predispositions, behaviors, and culture, i.e., the context of the community, to ensure and enhance realism of the model outcomes.