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Researching Innovative Capacity of Local Subsidiaries in Selected CEE Countries

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Technology Development

Part of the book series: Innovation, Technology, and Knowledge Management ((ITKM))

Abstract

The aim of the paper is to research the innovative capacities of local subsidiaries in selected CEE countries. We define innovative capacity as an ability of conducting innovation activities with innovation output variables i.e. innovation products and/or processes as the visible results of innovation inputs i.e. innovation investments. We found that the determinants of innovation input differ from the determinants of innovation output. The Innovation outputs variables are affected by productivity variables. On the other hand, local subsidiaries as a knowledge source for other unit of MNEs group as well as SMEs as a type of the ownership affect the innovation input determinants. However, similarities between innovation input and innovation output exist between business functions i.e. process engineering appear in both cases as determinants.The innovation performance measured by productivity are strongly reliant on local subsidiaries performance—changes in value in earning before interests and taxes, where investments into resources related to technologies are crucial i.e. differences in number of R&D employees between 2005 and 2002 and difference in the annual expenditure on R&D and innovation as a percentage of total sales positively influence local subsidiaries performance.

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Notes

  1. 1.

    Kamman [53] defines capacity in two ways. First, as the maximum volume of entities that: (a) can pass through a facility in a given time of period; (b) a node can produce in a given time period; (c) a node can absorb in a given time period; (d) a node can put through from one facility into another facility in a given time period; (e) can pass through the network between nodes, making use of as many facilities as is required, in a given time period; (f) does not upset the coordinating mechanism, leading to entropy, chaos or a loss in power vis-à-vis other network/nodes. Second, capacity refers to the maximum of any usually physically variable it can endure, resist, contain, or absorb, without losing its prime important task it was designed for.

  2. 2.

    Thus creation of various type of innovation cooperation with other firms and/or organisations is a normal sequence enabling these activities.

  3. 3.

    The firms are a source of the innovation.

  4. 4.

    According to UNCTAD [54], developing and transition economies, for the first time, attracted more than half of global FDI in 2010. Within the group, Eastern Asia countries considerably differ from Latin America and Central and Eastern European (CEE) countries. On the global level China belongs to countries that make up the bulk of the world’s surplus, whereas Latin America countries and CEE countries belong to the deficit countries in terms of International trade. Therefore, proportion and characteristics of the FDI and innovation activities a reconnected with characteristics of the national economies, where an export oriented economy such as China presents a more fruitful area for enhancing innovation activities in comparison to the national economies which belong to the deficit economies (e.g. CEE countries).

  5. 5.

    It is well established in the literature describing CEE countries and their transition that the entry of multinational corporations (MNCs) facilitated enterprise restructuring [55], export competitiveness [56] and productivity growth [57] as the most important factors influencing the integration of CEECs within the global markets.

  6. 6.

    It depends on technology acquisition, innovation strategy of their owner, level of competitiveness (cf. Aghion et al. [48]).

  7. 7.

    There is another concept similar to technological capabilities (Richardson [58]) defined as appropriate knowledge, experience and skills needed by firms and organisations to introduce new products and forms of organisations.

  8. 8.

    International production theory, market imperfections theory and Internalization theory are approaches within the theory of the FDI (Morgan and Katsikeas[16]: 70). International production theory argues the propensity of a firm to initiate foreign production will depend on the specific attractions of its home country compared with resource implications advantages of locating in another country [59]; The firm’s decision to invest overseas is explained as a strategy to capitalize on certain capabilities and not shared with foreign competitors is a basis of market imperfection theory; Extension of the direct operations of the firm and bringing under common ownership and control the activities conducted by intermediate markets that link the firm to customers is a basis of internationalization theory (cf. [60]).

  9. 9.

    We understand capabilities as the fim’s specific knowledge used to utilize the resources within the firms (Amit and Shoemaker [61], Makadok [62]). That is in line with Teece et al. [63] explanation fo the concept the dynamic capabilities which describe how the firms effectively use the resources within their strategic context.

  10. 10.

    Explanatory factors for the differences in the prices of the final products include the differences in sectoral structures of a national economy, and on the firm level functional structure (Steffen and Stephan [64]).

  11. 11.

    Country selection is a result of participation in the U-KNOW project, partially financed by the European Commission (EC) Framework Programme 6 (contract nr CIT5-028519).

  12. 12.

    Appendix 1 consists of the list of selected variables.

  13. 13.

    In Croatia the survey took place in April and May 2007. Therefore the analysed years are different. In analysed countries the survey took place in 2002 and 2005 whereas in case of Croatia, the survey was conducted in 2003 and 2006.

  14. 14.

    The variables within the models appear in various forms: nominal variables, categorical variables, percentage variables and binominal variables, where we try to analyze the static dimension of the variables. Calculating the percentage difference between categorical variables allows for the inclusion of the dynamic characteristics of the variables in the analysed period.

  15. 15.

    Researching the business function division between Multinational national enterprises (MNE) and local subsidiaries Aralica et al. [65] found that knowledge intensive functions, such as strategic management and process engineering are under control of the MNE whereas production of innovative products are controlled by local subsidiaries.

  16. 16.

    General information data and Business function data could be found in the Appendix 2, Table 14.3.

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Author information

Authors and Affiliations

  1. Engineering and Technology Management Department, Portland State University, Portland, OR, USA

    Tugrul U. Daim

  2. Institute of Economics, Zagreb, Croatia

    Zoran Aralica

  3. University of Zagreb, Zagreb, Croatia

    Marina Dabić

  4. Istanbul Technical University, Istanbul, Turkey

    Dilek Özdemir & A. Elvan Bayraktaroglu

Authors
  1. Tugrul U. Daim
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  2. Zoran Aralica
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  3. Marina Dabić
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  4. Dilek Özdemir
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  5. A. Elvan Bayraktaroglu
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Correspondence to Tugrul U. Daim .

Editor information

Editors and Affiliations

  1. Engineering and Technology Management, Portland State University, Portland, Oregon, USA

    Tugrul U. Daim

  2. Intel Corporation, Hillsboro, Oregon, USA

    Ramin Neshati

  3. Nike, Inc., Beaverton, Oregon, USA

    Russell Watt

  4. Tri Quint Semiconductos, Hillsboro, Oregon, USA

    James Eastham

Appendices

Appendix 1

Table 14.1 Descriptive analysis of the selected variables
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Appendix 2: List of the Variables in the Models

Table 14.2 Dependent variables in the modelsa
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Table 14.3 Dependent and independent variables in the models
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Table 14.4 Independent variables in the models
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Appendix 3: Models Results

Fig. 14.1
figure 1

Model one—multiple regression innovation output dependent variables

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Fig. 14.2
figure 2figure 2

Model two—multiple regression innovation input dependent variables

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Fig. 14.3
figure 3

Model three—multiple regression Innovation performance dependent variable

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Daim, T.U., Aralica, Z., Dabić, M., Özdemir, D., Bayraktaroglu, A.E. (2014). Researching Innovative Capacity of Local Subsidiaries in Selected CEE Countries. In: Daim, T., Neshati, R., Watt, R., Eastham, J. (eds) Technology Development. Innovation, Technology, and Knowledge Management. Springer, Cham. https://doi.org/10.1007/978-3-319-05651-7_14

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