1. To develop an understanding of the seven concepts of this book’s unifying framework.

2. To link specific types of transfers of firm-specific advantages (FSAs) across borders with the four corresponding multinational enterprise (MNE) archetypes of administrative heritage.

3. To describe the various motivations for foreign direct investment (FDI) and to explain the linkages among non-location-bound (or internationally transferable) FSAs, location-bound (or non-transferable) FSAs, and location advantages within each of the four MNE archetypes.

4. To define the ten often-observed patterns of FSA development and resource recombination in international business.

5. To explain the need for complementary resources of external actors, including those in the MNE’s stakeholder network and ecosystem, and the potential reasons for bounded rationality and bounded reliability when doing international business.

This concluding chapter discusses the strengths and limitations of Hierarchical Modelling of Species Communities (HMSC) in light of the results presented in this book. Concerning the strengths, the chapter notes that HMSC is a unifying framework that encompasses classic approaches such as single-species distribution models and model-based ordinations as special cases, which hence provides simultaneous inferences at the species and community levels. As another key strength, the chapter notes that HMSC can be applied to many kinds of study designs (including hierarchical, temporal or spatial) and many types of data (such as presence–absence, counts and continuous measurements). The chapter further emphasises that HMSC offers the general advantages of model-based approaches, such as tools for model validation and prediction, and that it is especially well suited for predictive modelling of communities with sparse data. Concerning the limitations, the chapter discusses three areas where future development is needed: a broader set of data models, a broader array of model structures related to various ecological and evolutionary processes, and improved computational efficiency.