PART I: OVERVIEW OF THE THESIS
5. CONCLUSIONS
5.4. Limitations and Future research
The focus of the thesis is on the influences of external factors on open innovation adoption, based on the theory of innovation systems (including institutional theory) and explained further through a study of national culture. The restriction to these theories only limits the study in terms of including other possible influencing factors. However, the case study approach for primary data collection targets a counterbalance to this aspect by keeping open questions in case new items should be mentioned.
Since the study focuses on external impacts, the internal company processes are acknowledged but not viewed separately in this context. Hence, the influence of the external environment is viewed in absolute terms, disregarding the internal company processes. The simultaneous influence of internal and external environments might provide somewhat different results, and should be addressed by future research.
Further limitation is derived from the data collection: 1) purposive sampling in case selection allowed for analysing only companies which are involved in open innovation to some extent, whereas studying completely closed companies and their barriers could bring additional insights on companies’ reluctance to embrace open innovation. 2) Misbalanced country samples for Finland, China and Russia may be regarded as not representative enough and decrease the generalisability and reliability of findings, however, taking into account the total number of businesses operating in theses countries, the misbalance does not seem to lead to an unrepresentative sample. 3) The use of system dynamics as a method has some identified limitations, which need to be acknowledged. The greatest concern here is in the trade-off between the generality, the realism and level of detail in the model (Axelrod 1997). These aspects counterbalance one another, as e.g. higher realism in the model will lead to an accurate case description with quantitative predictions, but simultaneously to less generalisable results (Kortelainen, 2011). The research has also some limitations originating from both the method used and the nature of innovation. Since the model is conceptual and tests no real data, it can only be validated against existing literature. Moreover, in reality
innovation has a high stochastic element, but as traditional system dynamics suggests the deterministic model, the innovation had to be simplified, which of course has an impact on the results. Additional limitation related to simulation model building comes from a simplification of the underlying processes. This is justified by keeping the lower level of complexity of the model (Repenning 2001) less heavy, but on the other hand this also brings some limitations in terms of inclusion of more possible factors, defining system behaviour. 4) The country scores used as input data for the cultural parameter (Hofstede’s dimensions) are relative since societies are compared to other societies. The recent global processes should be taken into account prior to claiming the long-term stability of cultural measures.
Hence, to further advance the discussion initiated in this thesis, the next version of the model should be developed. Factors to be included might be:
a) quantitative data,
b) more precise and explicit factors unlike those used in this study (e.g. IPR can be divided into cost, level of protection, complexity to file the patent etc.);
c) additional cultural dimensions from Hofstede’s datasets (power distance, uncertainty avoidance etc.), which will give a better insight into managerial implications and the human side of open innovation in the companies.
In order to better understand the influence of environment, the identification of the locus of the discovered tipping point would be useful; a more sophisticated mathematical model will thus be needed.
The basic assumption underlying this research is that open innovation is beneficial to companies; hence, interesting results may come from the targeting a simulation of the losses from opening up under certain environments (what sort of environment does it take to start losing from OI). Additionally, to eliminate the bias of comparing different companies with different procedures, the study of one company and its performance in different environments (e.g. subsidiaries of MNC) should be performed.
This thesis only starts the discussion on the role of culture in the spreading of open innovation, hence further research into the role of culture and mindsets on open innovation should be conducted, including the role of cultural proximity/distance in open innovation cooperation, technology transfer and partner selection. Moreover, since this thesis makes a claim that cultural differences do influence the emergence of certain open innovation associated barriers, this claim might be further validated by a larger number of countries and higher sample sizes. To tackle the cultural factors, more research on a human resources approach in order to open innovation might be of considerable value.
On the other hand, since the results also suggest that the importance of an appropriability regime may differ in the buying and selling sides of knowledge, the effects of property rights protection and its relationship to other structural issues ought to be more fully explored in future research.
Overall, a more complex understanding of the relationships between environmental factors are needed, together with test on real datasets, which would give a deeper understanding of what the environmental drivers to open innovation performance are. In addition, this data should be further compared with a real environment analysis, taking a closer look into the innovation systems and regulations supporting every relational claim.
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