- Email: [email protected]
The Economics and Politics of Process Innovation and the Sustainable Urban Development
Available online at www.sciencedirect.com
ScienceDirect Procedia Engineering 161 (2016) 2229 – 2233
World Multidisciplinary Civil Engineering-Architecture-Urban Planning Symposium 2016, WMCAUS 2016
The Economics and Politics of Process Innovation and The Sustainable Urban Development Marek Vokouna,* a
Department of management, Institute of Technology and Business in ýeské BudČjovice, 370 01 ýeské BudČjovice, Czech Republic
Abstract The paper analyses process innovators and non-innovators and their characteristics and strategies. The sustainable urban development is dependent on many factors. One of them is the ability of large companies to adapt to changes. The likelihood of engaging continuous process innovation is higher for larger firms in the Czech manufacturing industry. Large companies are important part of region and their instability can negatively affect the whole region. Being part of a business net (part of a group, innovation cooperation activities) is a positive determinant of process but only in certain stages of innovation process. The public policy aimed at regional technological hubs and networks can provide necessary boost for regional development rather than grants and public subsidies for innovation projects. © Published by Elsevier Ltd. This © 2016 2016The TheAuthors. Authors. Published by Elsevier Ltd. is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of WMCAUS 2016. Peer-review under responsibility of the organizing committee of WMCAUS 2016 Keywords: process innovation; business risks; urban planning; sustainability.
1. Introduction The paper analyses process innovators and their characteristics. Due to the accelerating pace of technological advancement (smart cities, open innovation, science parks, modern transport systems etc.) there is a need for innovative ideas for moving humankind into the age of sustainability. The firms have to incorporate processes which ensures sustainability of the firm and the company’s environment. The aim is to look at strategies that proved to be business risks reducing in the Czech manufacturing industry in times of economic crisis and analyze different strategies
* Corresponding author. Tel.: +420387 842 154. E-mail address: [email protected]
1877-7058 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of WMCAUS 2016
doi:10.1016/j.proeng.2016.08.820
2230
Marek Vokoun / Procedia Engineering 161 (2016) 2229 – 2233
firms and process innovators pursue. Hypotheses are related both to process innovators characteristics and cost efficiency strategies. This paper agrees with the analysis of Writh [23] which recommends engineers to become innovative thinkers and public policies and regional policies must encourage incentives in research and development that leads to innovation and the sustainable urban development. This paper tries to entangle the possibilities for public policies. According to the Oslo manual [16] and the Frascati manual [15], four types of innovation are recognized. Product, process, organizational, and marketing innovation are viewed by firms as new-to-the-firm, new-to-the-local market, and finally the world's first introduction to the global market. Such typology is a useful simplification. However, the field of innovation is not black and white, and a firm, an institution, even a government is not exactly a trustworthy reviewer and reporter of its financial and innovation activities. The strategy to innovate processes (or organizational setting) is in neoclassical economic theory seen as cost reducing in the market with imperfect competition [9]. A perfect competitor would not gain from a process innovation; it would only increase the consumer surplus. A monopoly would gain higher profit from a process innovation and is therefore motivated to do it. This process innovation strategy to avoid business risks is very appealing in times of economic crises. The idea that innovation influences economic fluctuations is quite old. In addition to Schumpeter's [19] analysis of the role of innovation, Schmookler [18] came to the hypothesis that there is a relationship between innovative behavior of entrepreneurs and the level of aggregate demand. Firms are motivated to speculate and adjust their conditions appropriately (lead time, intellectual property rights protection). In times of an economic boom they expect higher sales and want to introduce as many innovated goods and services as possible (product innovation). In times of crisis they are more likely to focus further at cost reduction, i. e. process and organizational innovation. Empirical testing suggests that demand theories are more plausible [8]. This paper attempts to entangle some of the hypotheses related to the process innovation in times of economic crisis. 2. Innovation of Processes in Times of Economic Crises Uncertainty, and external shocks are a common phenomenon in the new millennia and firms have to account for the dynamics and fluctuation. It is a reality they have to respect if they want to survive. This hostile environment is sometimes influenced by governments and related public institutional infrastructure. Innovation and recognizing business risks and opportunities (finding solutions) in such an environment is a strategic game. There are several ways the game is played. In case of process innovation, the best strategy is to cooperate with suppliers and universities [14]. On average, there is a positive relationship between innovation activities and productivity at the firm level among European firms [10], [13]. In our research we are more interested in process innovators. These innovation activities are closely related to the firm’s level of information and communication (ICT) capabilities especially when dealing with customer services [5] or RFID technology [3]. Process innovation is also closely related to export capabilities of a firm after a product innovation is successfully introduced in the national market [2]. Process innovation is a long term strategic decision dealing with core firm foundations. In comparison, a product or a marketing innovation can be often seen as a more tactical short term decision or even a consequence of good process innovation management in the past. Another difference is that process innovation among SMEs heavily relies on the external sources [12]. There are studies finding positive relationship between organizational innovation and firm managerial performance including specialization, professionalism, technical knowledge, and communication etc. [7], and production performance [20] in the manufacturing industry. There are different ways how efficiency can be measured [21] but this paper will use standard labour productivity. To fill the gap in the area of process and organizational innovation we are going to address 2 main hypotheses related to process innovation. There will be other descriptive characteristic of innovation process of the process innovation involved which are associated with so called technology-demand-push and demand-pull factors. (1) Decision to engage in process innovation is more probable among larger firms in the Czech manufacturing industry between 2006 and 2010. (2) Being part of a business net (part of a group, innovation cooperation activities) is a positive determinant of process innovation in the Czech manufacturing industry between 2006 and 2010.
Marek Vokoun / Procedia Engineering 161 (2016) 2229 – 2233
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3. Data and Method Two Community Innovation Survey (CIS) waves (2008, and 2010) were used for the analysis. Data from CZSO questionnaires were used. They are labelled: “TI”, “P5-01”, and “VTR5-01” in selected years. There was about a 23 % data loss (empty cells, zero employees, zero sales, innovators with zero R&D expenditures) for the Heckman procedure (see Method). For the 3SLS procedure (see Method) about 22 % more data was lost due to the joining of financial data. The firm level innovation data and financial statements are joined using so called “pseudo-ID”, which is a unique identification number, which allows us to merge the otherwise anonymized CZSO questionnaires (“TI”, “P5-01”, and “VTR5-01”) together. This unique opportunity allows us to build a panel dataset using 2 CIS waves. In the analysis section both the crosssectional approach and panel estimation is used. Data describe manufacturing industry quite well, there are around 43 % of foreign companies and the average firm has 308 employees. But SMEs are underrepresented and the dataset lacks micro enterprises. We are reporting raw data since the recursive system of equations (see method) allows for empty or zero observations in the data sample. Unreported variables are presented in the appendix section and are based on the standardized CIS questionnaire (hampering factors, demand “pull” and technology “push” variables). The industry technology classification (high-tech to low-tech) follows the OECD classification [17]. We will use a modified method and estimation strategy similar to the one used by Castellacci [4] and Crépon, Duguet and Mairesse [6]. The first step (decision) is a Heckman procedure which is also known as generalized procedure and the second step is a fixed effects 3SLS estimator [22]. 4. Results The first stage of results [22] can be interpreted as the percentage point change in the probability to innovate processes, ceteris paribus, and at the mean value. The second stage results (Appendix 1, Tab. 3., model 2) are based on the standard OLS on average ceteris paribus interpretation. The last two stages are also based on OLS (Appendix 1, Tab. 4., Models 3 and 4). The base, the average firm in the results was usually a Czech firm (not a multinational), oriented towards local markets (not EU and World markets), not a part of a group of companies, a low-tech industry firm (OECD classification) for the 3SLS procedure and high –tech firm for the Heckman procedure, and wasn’t cooperating in innovating activities (only in 3SLS procedure). Multinationals (MNEs) engaged less in process innovation which is to some extent consistent with previous research [25], [22]. Probability to innovate was about 21.5 % less between 2003 and 2010 than a base firm which is a local high-tech firm. Positive effect of the variable “being part of a group of companies” is again to some extent consistent with previous research. Being part of a functioning net or a bigger structure is related to higher probability of process innovation activities. Positive effect is also observable in bigger companies. The likelihood of performing process innovation rises proportionally with firm size. With every 10 % increase in firm size the probability proportion is 0.035 percentage points higher. In more concentrated markets, above the HHI value of 7250, there is even negative probability of process innovation activities. The highest probability point is around the HHI value of 3590 which describes rather competitive market. International competition also has a positive effect on process innovation activities. Medium to high-tech industry firms are those usually related to car industry. Their probability to engage in process innovation is higher in comparison to high-tech industries. One of the explanation is the competition among those firm and also their need to adapt to the supply-chain processes of large automotive companies. The second stage of estimation process (Appendix 1, Tab. 3., Model 2) is focused on R&D intensity of process innovators. We can see that smaller firms spend more per employee than larger firms. This ratio is again proportional and with 10 % increase in form size there is 1.93 % decrease in R&D per employee. The R&D intensity variable is without the machinery and equipment costs. The market concertation variables were jointly statistically significant and exhibit decreasing returns to scale. In more concentrated markets, above the value of 4180 HHI, the expenditures on R&D activities are lower and we can
2232
Marek Vokoun / Procedia Engineering 161 (2016) 2229 – 2233
observe negative relationship. The highest point (of the HHI functional relationship) is around the value of 2100 HHI which describes a very competitive market where a firm is spending more on R&D activities. In the second stage there are significant technology push and demand pull factors which are important for process innovators. Important information source are suppliers. Firms which perceived them as important spend more on R&D activities. Lesser expenditure costs have firms which perceived information from universities and inside the firm. Firms which perceive technological pressures from new markets and have a need to upgrade their flexibility spend less on R&D activities. Other factors were not statistically significant. The third and fourth stage is characterized by sales of innovated goods and services per employee (innovation appropriability) and labour productivity (sales per employee) functions. In the third equation in [22], the amount of sales of innovated goods and services per employee (innovation output) depended positively on R&D expenditures per employee, R&D cooperation, market orientation. The input-output innovation elasticity is very low for process innovators. For every 10 % increase in R&D expenditures per employee the additional increase in sales form innovated goods per employee is only 0.53 %. There is no relationship between firm size, being part of a group, market concentration, and labour productivity and sales of innovated goods and services per employee among process innovators. We can observe that cooperation is again a positive factor of appropriability along with market orientation. In the fourth equation in [22], the amount of sales of goods and services per employee (labour productivity) depended positively on fixed assets per employee and being part of a group of companies. There is no relationship between sales of innovated goods and services per employee and labour productivity among process innovators. Process innovation is not always linked with increase in sales and this result suggests that process innovation is more aimed at cost reduction activities. The HHI variables exhibit decreasing returns to scale. In more concentrated markets, above the value of 6 000 HHI, the labour productivity is lower and we can observe a negative relationship. The highest point (of the HHI functional relationship) is around the value of 3 000 HHI which describes a very competitive market where a firm has higher labour productivity. Smaller process innovators have higher labour productivity. Hampering factors were again significant and firms which perceived to have lack of information and qualified personnel, and see the costs of innovating to be very high have, on average, lower labour productivity. 5. Conclusion We can confirm that the likelihood of engaging process innovation is higher for larger firms in the Czech manufacturing industry between 2006 and 2010. Larger firms are aware of the need to reduce costs and are typical companies characterized by continuous process of process innovation. Being part of a business net (part of a group, innovation cooperation activities) is a positive determinant of process innovation in the Czech manufacturing industry between 2006 and 2010, but only in certain stages. Cooperation on innovation activities is a positive determinant of appropriable innovation and being part of a group is positive determinant in the first and last stage of innovation process. The recommendation for public policy and regional policies is to encourage the cooperation of firms on innovation activities and decrease all level of bureaucracy which can hamper innovation projects. References [1] ANDERSSON, M., JOHANSSON, B., KARLSSON, C., LÖÖF, H. (2012). Innovation and Growth: From R&D Strategies of Innovating Firms to Economy-wide Technological Change, Oxford University Press, Oxford, 368 s. [2] BECKER, S.O., EGGER, P.H. (2013). Endogenous product versus process innovation and a firm’s propensity to export, Empirical Economics, vol. 44, no. 1, s. 329-354. [3] BUNDUCHI, R., WEISSHAAR, C., SMART, A.U. (2011). Mapping the benefits and costs associated with process innovation: The case of RFID adoption, Technovation, vol. 31, no. 9, s. 505-521. [4] CASTELLACCI, F. (2009). How does competition affect the relationship between innovation and productivity? Estimation of a CDM model for Norway, [online], MPRA Paper 27591, University Library of Munich, Germany. [Accessed 2 April 2012]. Retrieved from: http://ideas.repec.org/p/pra/mprapa/27591.html.
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[5] CHEN, J.-S., TSOU, H.-T. (2012). Performance effects of IT capability, service process innovation, and the mediating role of customer service, Journal of Engineering and Technology Management, vol. 29, no. 1, s. 71-94 [6] CREPON, B., DUGUET, E., MAIRESSE, J. (1998). Research, Innovation and Productivity: An Econometric Analysis at the Firm Level, Economics of Innovation and New Technology, vol. 7, no. 2, s. 115-158. [7] DAMANPOUR, F. (1991). Organizational innovation: A meta-analysis of effects of determinants and moderators, Academy of management journal, vol. 34, no. 3, s. 555–590. [8] GEROSKI, P.A., WALTERS, C.F. (1995). Innovative Activity over the Business Cycle, The Economic Journal, vol. 105, no. 431, s. 916-928. [9] GREENHALGH, C., ROGERS, M. (2009). Innovation, Intellectual Property, and Economic Growth, Princeton University Press, Princeton, 384 s. [10] HASHI, I., STOJýIû, N. (2013). The impact of innovation activities on firm performance using a multi-stage model: Evidence from the Community Innovation Survey 4, Research Policy, vol. 42, no. 2, s. 353-366. [11] HECKMAN, J.J. (1976). The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models, Annals of Economic and Social Measurement, vol. 5, no. 4, s. 475-492. [12] HERVAS-OLIVER, J.-L., SEMPERE-RIPOLL, F., BORONAT-MOLL, C. (2014). Process innovation strategy in SMEs, organizational innovation and performance: a misleading debate?, Small Business Economics, vol. 43, no. 4, s. 873-886. [13] MASSO, J., VAHTER, P. (2008). Technological innovation and productivity in late-transition Estonia: econometric evidence from innovation surveys, The European Journal of Development Research, vol. 20, no. 2, s. 240-261. [14] MULVEY, J.M., ROSENBAUM, D.P., SHETTY, B. (1997). Strategic financial risk management and operations research, European Journal of Operational Research, vol. 97, no. 1, s. 1–16. [15] OECD (2002). Frascati Manual, Organisation for Economic Co-operation and Development, Paris, 256 s. [16] OECD (2005). Oslo Manual, Organisation for Economic Co-operation and Development, Paris, 162 s. [17] OECD (2011). Classification of manufacturing industries into categories based on R&D intensities [online], Economic Analysis and Statistics Division, [Accessed 4 12 2013], Retrieved from: http://www.oecd.org/sti/ind/48350231.pdf. [18] SCHMOOKLER, J. (1966). Invention and economic growth, Harvard University Press, Harvard, 332. [19] SCHUMPETER, J. (1934). The theory of economic development: an inquiry into profits, capital, credit, interest, and the business cycle, Transaction Publishers, New Jersey, 324 s. [20] ULUSOY, G., GUNDAY, G., ALPKAN, L., KILIC, K. (2008). Effects of organizational innovations on firm’s production performance, Industrial Engineering and Engineering Management, 2008. IEEM 2008. IEEE International Conference [online], IEEE, s. 321–325. [Accessed 28 9 2015], Retrieved from: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4737883. [21] VOCHOZKA, Marek, Jan VÁCHAL a Jarmila STRAKOVÁ. Measuring technical efficiency using the example of construction companies. Ekonomika a management, Praha: PodnikohospodáĜská fakulta VŠE v Praze, 2015, Neuveden, þ. 2, s. ISSN 1802-8934, (in Czech). [22] VOKOUN, M. (2015). Innovation behaviour of firms in a small open economy: the case of the Czech manufacturing industry, Empirica, s. 129. [23] WRIGHT, D.W. (1996). Infrastructure planning and sustainable development, Journal of Urban Planning and Development, 122 (4), s 111117. [24] ZELLNER, A., THEIL, H. (1962). Three-Stage Least Squares: Simultaneous Estimation of Simultaneous Equations, Econometrica, vol. 30, no. 1, s. 54-78. [25] ZEMPLINEROVÁ, A., HROMÁDKOVÁ, E. (2012). Determinants of Firm Innovation, Prague Economic Papers, vol. 2012, no. 4, s. 487503.
ScienceDirect Procedia Engineering 161 (2016) 2229 – 2233
World Multidisciplinary Civil Engineering-Architecture-Urban Planning Symposium 2016, WMCAUS 2016
The Economics and Politics of Process Innovation and The Sustainable Urban Development Marek Vokouna,* a
Department of management, Institute of Technology and Business in ýeské BudČjovice, 370 01 ýeské BudČjovice, Czech Republic
Abstract The paper analyses process innovators and non-innovators and their characteristics and strategies. The sustainable urban development is dependent on many factors. One of them is the ability of large companies to adapt to changes. The likelihood of engaging continuous process innovation is higher for larger firms in the Czech manufacturing industry. Large companies are important part of region and their instability can negatively affect the whole region. Being part of a business net (part of a group, innovation cooperation activities) is a positive determinant of process but only in certain stages of innovation process. The public policy aimed at regional technological hubs and networks can provide necessary boost for regional development rather than grants and public subsidies for innovation projects. © Published by Elsevier Ltd. This © 2016 2016The TheAuthors. Authors. Published by Elsevier Ltd. is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of WMCAUS 2016. Peer-review under responsibility of the organizing committee of WMCAUS 2016 Keywords: process innovation; business risks; urban planning; sustainability.
1. Introduction The paper analyses process innovators and their characteristics. Due to the accelerating pace of technological advancement (smart cities, open innovation, science parks, modern transport systems etc.) there is a need for innovative ideas for moving humankind into the age of sustainability. The firms have to incorporate processes which ensures sustainability of the firm and the company’s environment. The aim is to look at strategies that proved to be business risks reducing in the Czech manufacturing industry in times of economic crisis and analyze different strategies
* Corresponding author. Tel.: +420387 842 154. E-mail address: [email protected]
1877-7058 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of WMCAUS 2016
doi:10.1016/j.proeng.2016.08.820
2230
Marek Vokoun / Procedia Engineering 161 (2016) 2229 – 2233
firms and process innovators pursue. Hypotheses are related both to process innovators characteristics and cost efficiency strategies. This paper agrees with the analysis of Writh [23] which recommends engineers to become innovative thinkers and public policies and regional policies must encourage incentives in research and development that leads to innovation and the sustainable urban development. This paper tries to entangle the possibilities for public policies. According to the Oslo manual [16] and the Frascati manual [15], four types of innovation are recognized. Product, process, organizational, and marketing innovation are viewed by firms as new-to-the-firm, new-to-the-local market, and finally the world's first introduction to the global market. Such typology is a useful simplification. However, the field of innovation is not black and white, and a firm, an institution, even a government is not exactly a trustworthy reviewer and reporter of its financial and innovation activities. The strategy to innovate processes (or organizational setting) is in neoclassical economic theory seen as cost reducing in the market with imperfect competition [9]. A perfect competitor would not gain from a process innovation; it would only increase the consumer surplus. A monopoly would gain higher profit from a process innovation and is therefore motivated to do it. This process innovation strategy to avoid business risks is very appealing in times of economic crises. The idea that innovation influences economic fluctuations is quite old. In addition to Schumpeter's [19] analysis of the role of innovation, Schmookler [18] came to the hypothesis that there is a relationship between innovative behavior of entrepreneurs and the level of aggregate demand. Firms are motivated to speculate and adjust their conditions appropriately (lead time, intellectual property rights protection). In times of an economic boom they expect higher sales and want to introduce as many innovated goods and services as possible (product innovation). In times of crisis they are more likely to focus further at cost reduction, i. e. process and organizational innovation. Empirical testing suggests that demand theories are more plausible [8]. This paper attempts to entangle some of the hypotheses related to the process innovation in times of economic crisis. 2. Innovation of Processes in Times of Economic Crises Uncertainty, and external shocks are a common phenomenon in the new millennia and firms have to account for the dynamics and fluctuation. It is a reality they have to respect if they want to survive. This hostile environment is sometimes influenced by governments and related public institutional infrastructure. Innovation and recognizing business risks and opportunities (finding solutions) in such an environment is a strategic game. There are several ways the game is played. In case of process innovation, the best strategy is to cooperate with suppliers and universities [14]. On average, there is a positive relationship between innovation activities and productivity at the firm level among European firms [10], [13]. In our research we are more interested in process innovators. These innovation activities are closely related to the firm’s level of information and communication (ICT) capabilities especially when dealing with customer services [5] or RFID technology [3]. Process innovation is also closely related to export capabilities of a firm after a product innovation is successfully introduced in the national market [2]. Process innovation is a long term strategic decision dealing with core firm foundations. In comparison, a product or a marketing innovation can be often seen as a more tactical short term decision or even a consequence of good process innovation management in the past. Another difference is that process innovation among SMEs heavily relies on the external sources [12]. There are studies finding positive relationship between organizational innovation and firm managerial performance including specialization, professionalism, technical knowledge, and communication etc. [7], and production performance [20] in the manufacturing industry. There are different ways how efficiency can be measured [21] but this paper will use standard labour productivity. To fill the gap in the area of process and organizational innovation we are going to address 2 main hypotheses related to process innovation. There will be other descriptive characteristic of innovation process of the process innovation involved which are associated with so called technology-demand-push and demand-pull factors. (1) Decision to engage in process innovation is more probable among larger firms in the Czech manufacturing industry between 2006 and 2010. (2) Being part of a business net (part of a group, innovation cooperation activities) is a positive determinant of process innovation in the Czech manufacturing industry between 2006 and 2010.
Marek Vokoun / Procedia Engineering 161 (2016) 2229 – 2233
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3. Data and Method Two Community Innovation Survey (CIS) waves (2008, and 2010) were used for the analysis. Data from CZSO questionnaires were used. They are labelled: “TI”, “P5-01”, and “VTR5-01” in selected years. There was about a 23 % data loss (empty cells, zero employees, zero sales, innovators with zero R&D expenditures) for the Heckman procedure (see Method). For the 3SLS procedure (see Method) about 22 % more data was lost due to the joining of financial data. The firm level innovation data and financial statements are joined using so called “pseudo-ID”, which is a unique identification number, which allows us to merge the otherwise anonymized CZSO questionnaires (“TI”, “P5-01”, and “VTR5-01”) together. This unique opportunity allows us to build a panel dataset using 2 CIS waves. In the analysis section both the crosssectional approach and panel estimation is used. Data describe manufacturing industry quite well, there are around 43 % of foreign companies and the average firm has 308 employees. But SMEs are underrepresented and the dataset lacks micro enterprises. We are reporting raw data since the recursive system of equations (see method) allows for empty or zero observations in the data sample. Unreported variables are presented in the appendix section and are based on the standardized CIS questionnaire (hampering factors, demand “pull” and technology “push” variables). The industry technology classification (high-tech to low-tech) follows the OECD classification [17]. We will use a modified method and estimation strategy similar to the one used by Castellacci [4] and Crépon, Duguet and Mairesse [6]. The first step (decision) is a Heckman procedure which is also known as generalized procedure and the second step is a fixed effects 3SLS estimator [22]. 4. Results The first stage of results [22] can be interpreted as the percentage point change in the probability to innovate processes, ceteris paribus, and at the mean value. The second stage results (Appendix 1, Tab. 3., model 2) are based on the standard OLS on average ceteris paribus interpretation. The last two stages are also based on OLS (Appendix 1, Tab. 4., Models 3 and 4). The base, the average firm in the results was usually a Czech firm (not a multinational), oriented towards local markets (not EU and World markets), not a part of a group of companies, a low-tech industry firm (OECD classification) for the 3SLS procedure and high –tech firm for the Heckman procedure, and wasn’t cooperating in innovating activities (only in 3SLS procedure). Multinationals (MNEs) engaged less in process innovation which is to some extent consistent with previous research [25], [22]. Probability to innovate was about 21.5 % less between 2003 and 2010 than a base firm which is a local high-tech firm. Positive effect of the variable “being part of a group of companies” is again to some extent consistent with previous research. Being part of a functioning net or a bigger structure is related to higher probability of process innovation activities. Positive effect is also observable in bigger companies. The likelihood of performing process innovation rises proportionally with firm size. With every 10 % increase in firm size the probability proportion is 0.035 percentage points higher. In more concentrated markets, above the HHI value of 7250, there is even negative probability of process innovation activities. The highest probability point is around the HHI value of 3590 which describes rather competitive market. International competition also has a positive effect on process innovation activities. Medium to high-tech industry firms are those usually related to car industry. Their probability to engage in process innovation is higher in comparison to high-tech industries. One of the explanation is the competition among those firm and also their need to adapt to the supply-chain processes of large automotive companies. The second stage of estimation process (Appendix 1, Tab. 3., Model 2) is focused on R&D intensity of process innovators. We can see that smaller firms spend more per employee than larger firms. This ratio is again proportional and with 10 % increase in form size there is 1.93 % decrease in R&D per employee. The R&D intensity variable is without the machinery and equipment costs. The market concertation variables were jointly statistically significant and exhibit decreasing returns to scale. In more concentrated markets, above the value of 4180 HHI, the expenditures on R&D activities are lower and we can
2232
Marek Vokoun / Procedia Engineering 161 (2016) 2229 – 2233
observe negative relationship. The highest point (of the HHI functional relationship) is around the value of 2100 HHI which describes a very competitive market where a firm is spending more on R&D activities. In the second stage there are significant technology push and demand pull factors which are important for process innovators. Important information source are suppliers. Firms which perceived them as important spend more on R&D activities. Lesser expenditure costs have firms which perceived information from universities and inside the firm. Firms which perceive technological pressures from new markets and have a need to upgrade their flexibility spend less on R&D activities. Other factors were not statistically significant. The third and fourth stage is characterized by sales of innovated goods and services per employee (innovation appropriability) and labour productivity (sales per employee) functions. In the third equation in [22], the amount of sales of innovated goods and services per employee (innovation output) depended positively on R&D expenditures per employee, R&D cooperation, market orientation. The input-output innovation elasticity is very low for process innovators. For every 10 % increase in R&D expenditures per employee the additional increase in sales form innovated goods per employee is only 0.53 %. There is no relationship between firm size, being part of a group, market concentration, and labour productivity and sales of innovated goods and services per employee among process innovators. We can observe that cooperation is again a positive factor of appropriability along with market orientation. In the fourth equation in [22], the amount of sales of goods and services per employee (labour productivity) depended positively on fixed assets per employee and being part of a group of companies. There is no relationship between sales of innovated goods and services per employee and labour productivity among process innovators. Process innovation is not always linked with increase in sales and this result suggests that process innovation is more aimed at cost reduction activities. The HHI variables exhibit decreasing returns to scale. In more concentrated markets, above the value of 6 000 HHI, the labour productivity is lower and we can observe a negative relationship. The highest point (of the HHI functional relationship) is around the value of 3 000 HHI which describes a very competitive market where a firm has higher labour productivity. Smaller process innovators have higher labour productivity. Hampering factors were again significant and firms which perceived to have lack of information and qualified personnel, and see the costs of innovating to be very high have, on average, lower labour productivity. 5. Conclusion We can confirm that the likelihood of engaging process innovation is higher for larger firms in the Czech manufacturing industry between 2006 and 2010. Larger firms are aware of the need to reduce costs and are typical companies characterized by continuous process of process innovation. Being part of a business net (part of a group, innovation cooperation activities) is a positive determinant of process innovation in the Czech manufacturing industry between 2006 and 2010, but only in certain stages. Cooperation on innovation activities is a positive determinant of appropriable innovation and being part of a group is positive determinant in the first and last stage of innovation process. The recommendation for public policy and regional policies is to encourage the cooperation of firms on innovation activities and decrease all level of bureaucracy which can hamper innovation projects. References [1] ANDERSSON, M., JOHANSSON, B., KARLSSON, C., LÖÖF, H. (2012). Innovation and Growth: From R&D Strategies of Innovating Firms to Economy-wide Technological Change, Oxford University Press, Oxford, 368 s. [2] BECKER, S.O., EGGER, P.H. (2013). Endogenous product versus process innovation and a firm’s propensity to export, Empirical Economics, vol. 44, no. 1, s. 329-354. [3] BUNDUCHI, R., WEISSHAAR, C., SMART, A.U. (2011). Mapping the benefits and costs associated with process innovation: The case of RFID adoption, Technovation, vol. 31, no. 9, s. 505-521. [4] CASTELLACCI, F. (2009). How does competition affect the relationship between innovation and productivity? Estimation of a CDM model for Norway, [online], MPRA Paper 27591, University Library of Munich, Germany. [Accessed 2 April 2012]. Retrieved from: http://ideas.repec.org/p/pra/mprapa/27591.html.
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