Abstract
Absorptive capacity is considered a main precondition for regional innovativeness. However, each region is unique, and analyzing its innovativeness requires an appropriate methodology. Within scientific discussion on the concept of the “region”, most of the scientific research analyzing absorptive capacity tends to be conducted for large or highly developed regions. Small countries (such as Lithuania) are considered indivisible regional units; therefore, there is still a lack of research to provide specific tools that could be adapted to assess regional absorptive capacity in a small country. Taking this into consideration, this chapter’s objective is to present a methodology for assessing RIS absorptive capacity that has been adapted to the context of regions in a small country (Lithuania). Its goals are as follows: (1) to explain the concept of absorptive capacity in the context of a RIS; (2) to provide the theoretical background for assessing absorptive capacity; (3) to present a methodology for assessing absorptive capacity in a small country (Lithuania); and (4) to explain the main results of the research, which was conducted for RIS in Lithuania using the methodology presented here. The scientific research methods used were an analysis of scientific literature, statistical analysis, and the SAW multiple criteria method. The research revealed that regions in a small country differ in their various indicators of absorptive capacity. Using the appropriate tool for assessment can also be a tool for identifying a region’s strong and weak points as well as for promoting or interfering with the absorption of external knowledge.
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Notes
- 1.
NUTS are a common classification of territorial units for EU regional statistics. Lithuania is classified as a NUTSII region. All information concerned with innovations is collected at the country level without distinguishing regional indicators.
- 2.
Regions classified as NUTSIII have a population of 150,000–800,000 inhabitants, and their area must range within 10–83.5 thousand km2. All ten of Lithuania’s counties are classified as NUTSIII regions (Commission Regulation… 2014).
- 3.
The method of criteria selection is used to verify and identify the sample in the population by selecting units according to a set of criteria that has been determined by the authors. This method is useful when the population is quite big and researchers wish to compare results.
- 4.
There were assumptions concerning the regional dimension that were made in order to formulate certain indicators (to calculate their numerical estimates). However, it is possible to discard these assumptions and to calculate more accurately in the case of access to more detailed information sources that can provide detailed statistics for the small country’s regions.
- 5.
All statistical data for the paper was acquired from two institutions—Statistics Lithuania (a government institution that collects, analyzes, and publishes statistical data and reports on the country’s industrial, commercial, financial, social, etc. activities and the environment) and the State Patent Bureau of the Republic of Lithuania (a government institution that implements the legalization and state protection of industrial property (inventions, designs, brands, typography, etc.) and the functions of the central industrial property office within the EU and the European Patent Organization). For the first source, databases and direct communication with the institution’s staff were used to get the necessary statistics. For the second, official monthly bulletins were analyzed.
- 6.
A polynomial trend function is a suitable tool for calculating missing estimates when numerical data with some fluctuations has been provided. Missing estimates are calculated (predicted) according to the value of the statistical data series’ coefficient of determination (R2). The prediction is more targeted when R2 is closer the value of 1 (R2 > 0.65 for this calculation and predicting the missing estimates in this paper).
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Juknevičienė, V., Mikolaitytė, J., Šaparnienė, D. (2018). Assessing the Absorptive Capacity of Regional Innovation Systems: A Case Study of Lithuanian Regions. In: Stejskal, J., Hajek, P., Hudec, O. (eds) Knowledge Spillovers in Regional Innovation Systems. Advances in Spatial Science. Springer, Cham. https://doi.org/10.1007/978-3-319-67029-4_2
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