5. Conclusion
5.4. Limitations and Future Research
The research presented in this dissertation is not without limitations—some concern policy instruments as the unit of analysis and others the methodologies and research designs selected for one or more of the papers. First, the study of targeted actor constellations and civil society involvement at the level of instruments as presented in the first paper is not revelatory about actor constellations at the level of policy mixes for grand challenges. If combinations of instruments address a specific challenge, the heterogeneity of actors might be achieved at the level of mixes rather than instruments. Therefore, future researchers may consider analysing actor constellations at the level of thematically delineated policy mixes for grand challenges.
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Second, the study of innovation policy mixes in the second and third papers is also focused on policy instruments as the unit of analysis, thereby arguably understating the complexity of innovation policy mixes that contain policy strategies and policy processes as well (Rogge and Reichardt 2016). The arguments for focusing the analysis have been presented in the analytical framework—the restriction to one type of unit of analysis seems necessary for large-n research, and strategies should leave traces in the instrumentation of policy mixes. Moreover, the research designs of the second and third papers account for factors unrelated to innovation problems influencing innovation problems, and in that sense indirectly account for the role of policy processes. Nevertheless, analyses of the data on policy strategies and debates from the STIP Compass would be of great interest and could complement those conducted in the context of this dissertation.
A methodological limitation is that topics reflecting latent patterns in the textual data on policy instruments are imperfect measures. Instruments can be characterised by the goals they are meant to achieve and by their means to achieve them. The second and third papers have used topic modelling to analyse textual data containing information on both these aspects of instruments without discriminating between them. Topic modelling as applied in this dissertation thus ignores the internal differentiation of the concept of policy instruments. Moreover, a caveat inherent to bottom-up classification schemes is the risk of producing categories that are difficult to interpret (Chang et al. 2009). Thus, it is likely that in policy mix mappings based on topic modelling, some topics will be more difficult to interpret than others, which could be problematic for topic-based instrument comparisons. In sum, while topic modelling is a highly useful explorative method, future research could systematically compare different approaches to measuring innovation policy mixes and analyse their complementarities.
Finally, there are at least two directions for further theoretical development. One direction concerns the temporal dimension of the relationships under study. The first paper has ignored this dimension, whereas the second and third papers have considered it only to a limited degree. Yet, the first paper could be extended to consider the co-evolution of policy rationales and instrument design in the case of grand challenges over time, and the second and third papers would benefit from considering temporal dynamics in policy mixes. This would permit them to move from analysing associations among policy instruments
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and factors related to their design or choice to questions about the relationships among inputs and outputs of innovation policy design. Hence, future research might consider how to better exploit the data regarding information on temporal aspects of policies.
A second direction for further theoretical development concerns the local and regional dimensions of innovative and entrepreneurial activity (Audretsch and Feldman 2004; Feldman 2014). The second and third papers have studied innovation policy mixes at the country level, employing an analytical perspective facilitated by the dataset used. Yet, spatial differentiation might be key to better understanding how innovation policy relates to entrepreneurial activity and innovation capability. Future research could explore how to better consider information about the local or regional scope of policy instruments included in this dataset or include additional data on economic structures at the sub-national level in the analysis.
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