Using categorical DEA to assess the effect of subsidy policies and technological learning on R&D efficiency of it industry
Abstract
Government subsidies are an important policy tool that can help firms develop technological learning, and this technological learning effect plays a key role in firms’ research and development (R&D) efficiency. Thus, this study develops a two-stage approach to illustrate the effect of subsidy policies and technological learning on R&D efficiency in the information technology (IT) industry. The technological learning effect in 128 firms in the IT industry from 2008 to 2015 was measured using the learning experience curve. Subsequently, government R&D subsidy intensity was considered as a categorical variable, and this estimated result was treated as an intangible input into a data envelopment analysis (DEA) structure to evaluate R&D efficiency in 2015. This study makes three major contributions. First, the developed approach incorporates the effect of subsidy policies and technological learning into the DEA structure. Second, the empirical results demonstrate the appropriateness of incorporating subsidy policies and technological learning into evaluations of R&D efficiency. Finally, our results identify the key sources of inefficiency as a shortfall in the number of patents and a lack of technological learning. Based on these key findings, some improved strategies were recommended to decision makers.
First published online 19 November 2019
Keyword : data envelopment analysis, government subsidies, information technology industry, learning experience curve, technological learning, R&D efficiency
How to Cite
Yeh, L.-T., & Chang, D.-S. (2020). Using categorical DEA to assess the effect of subsidy policies and technological learning on R&D efficiency of it industry. Technological and Economic Development of Economy, 26(2), 311-330. https://doi.org/10.3846/tede.2019.11411
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Choi, J., & Yeniyurt, S. (2015). Contingency distance factors and international research and development (R&D), marketing, and manufacturing alliance formations. International Business Review, 24(6), 1061-1071. https://doi.org/10.1016/j.ibusrev.2015.04.007
Chung, S. (2001). The learning curve and the yield factor: the case of Korea’s semiconductor industry. Applied Economics, 33(4), 473-483. https://doi.org/10.1080/00036840122474
Coccia, M. (2009). What is the optimal rate of R&D investment to maximize productivity growth? Technological Forecasting and Social Change, 76(3), 433-446. https://doi.org/10.1016/j.techfore.2008.02.008
Czarnitzki, D., & Hussinger, K. (2018). Input and output additionality of R&D subsidies. Applied Economics, 50(12), 1324-1341. https://doi.org/10.1080/00036846.2017.1361010
Dodgson, M. (1991). The management of technological learning: lessons from a biotechnology company. Berlin: Walter de Gruyter. https://doi.org/10.1515/9783110867749
El-Mashaleh, M., Al-Smadi, B. M., Hyari, K. H., & Rababeh, S. M. (2010). Safety management in the Jordanian construction industry. Jordan Journal of Civil Engineering, 4(1), 117-120.
Grübler, A., & Messner, S. (1998). Technological change and the timing of mitigation measures. Energy Economics, 20(5), 495-512. https://doi.org/10.1016/S0140-9883(98)00010-3
Guan, J., Zuo, K., Chen, K., & Yam, R. C. M. (2016). Does country-level R&D efficiency benefit from the collaboration network structure? Research Policy, 45(4), 770-784. https://doi.org/10.1016/j.respol.2016.01.003
Haas, D. A., & Murphy, F. H. (2003). Compensating for non-homogeneity in decision-making units in data envelopment analysis. European Journal of Operational Research, 144(3), 530-544. https://doi.org/10.1016/S0377-2217(02)00139-X
Hall, B., & Van Reenen, J. (2000). How effective are fiscal incentives for R&D? A review of the evidence. Research Policy, 29(4), 449-469. https://doi.org/10.1016/S0048-7333(99)00085-2
Han, Y. J. (2007). Measuring industrial knowledge stocks with patents and papers. Journal of Informetrics, 1(4), 269-276. https://doi.org/10.1016/j.joi.2007.06.001
Hanna, N., Boyson, S., & Gunaratne, S. (1999). The east Asian miracle and information technology: strategic management of technological learning. Washington: World Bank Publications.
Hashimoto, A., & Haneda, S. (2008). Measuring the change in R&D efficiency of the Japanese pharmaceutical industry. Research Policy, 37(10), 1829-1836. https://doi.org/10.1016/j.respol.2008.08.004
Hitt, M. A., Ireland, R. D., & Lee, H. U. (2000). Technological learning, knowledge management, firm growth and performance: an introductory essay. Journal of Engineering and Technology Management, 17(3), 231-246. https://doi.org/10.1016/S0923-4748(00)00024-2
Ho, L. A., & Kuo, T. H. (2010). How can one amplify the effect of e-learning? An examination of hightech employees’ computer attitude and flow experience. Computers in Human Behavior, 26(1), 2331. https://doi.org/10.1016/j.chb.2009.07.007
Hsu, C. W., & Chiang, H. C. (2001). The government strategy for the upgrading of industrial technology in Taiwan. Technovation, 21(2), 123-132. https://doi.org/10.1016/S0166-4972(00)00029-8
Hsu, F. M., & Hsueh, C. C. (2009). Measuring relative efficiency of government-sponsored R&D projects: a three-stage approach. Evaluation and Program Planning, 32(2), 178-186. https://doi.org/10.1016/j.evalprogplan.2008.10.005
Hudon, M., & Traca, D. (2011). On the efficiency effects of subsidies in microfinance: an empirical inquiry. World Development, 39(6), 966-973. https://doi.org/10.1016/j.worlddev.2009.10.017
Kelm, K. M., Narayanan, V., & Pinches, G. E. (1995). Shareholder value creation during R&D innovation and commercialization stages. Academy of Management Journal, 38(3), 770-786. https://doi.org/10.2307/256745
Kessler, E. H., Bierly, P. E., & Gopalakrishnan, S. (2000). Internal vs. external learning in new product development: effects on speed, costs and competitive advantage. R&D Management, 30(3), 213-224. https://doi.org/10.1111/1467-9310.00172
Kouvaritakis, N., Soria, A., & Isoard, S. (2000). Modelling energy technology dynamics: methodology for adaptive expectations models with learning by doing and learning by searching. International Journal of Global Energy Issues, 14(1-4), 104-115. https://doi.org/10.1504/IJGEI.2000.004384
Lall, S. (2000). The technological structure and performance of developing country manufactured exports, 1985‐98. Oxford Development Studies, 28(3), 337-369. https://doi.org/10.1080/713688318
Lev, B., Radhakrishnan, S., & Zhang, W. (2009). Organization capital. Abacus, 45(3), 275-298. https://doi.org/10.1111/j.1467-6281.2009.00289.x
Li, N. (2014). Labor market peer firms. University of Chicago, The University of Chicago Booth School of Business.
Li, N. (2017, November 1). Who are my peers? Labor market peer firms through employees’ internet co-search patterns (Rotman School of Management Working Paper No. 2558271). Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2558271
Linton, J. D., Walsh, S. T., & Morabito, J. (2002). Analysis, ranking and selection of R&D projects in a portfolio. R&D Management, 32(2), 139-148. https://doi.org/10.1111/1467-9310.00246
Lo, S.-F., & Lu, W.-M. (2009). An integrated performance evaluation of financial holding companies in Taiwan. European Journal of Operational Research, 198(1), 341-350. https://doi.org/10.1016/j.ejor.2008.09.006
Lyu, K., Bian, Y., & Yu, A. (2018). Environmental efficiency evaluation of industrial sector in China by incorporating learning effects. Journal of Cleaner Production, 172, 2464-2474. https://doi.org/10.1016/j.jclepro.2017.11.163
Moorman, C., Wies, S., Mizik, N., & Spencer, F. J. (2012). Firm innovation and the ratchet effect among consumer packaged goods firms. Marketing Science, 31(6), 934-951. https://doi.org/10.1287/mksc.1120.0737
Nakata, T., Sato, T., Wang, H., Kusunoki, T., & Furubayashi, T. (2011). Modeling technological learning and its application for clean coal technologies in Japan. Applied Energy, 88(1), 330-336. https://doi.org/10.1016/j.apenergy.2010.05.022
Ngwenyama, O., Guergachi, A., & McLaren, T. (2007). Using the learning curve to maximize IT productivity: a decision analysis model for timing software upgrades. International Journal of Production Economics, 105(2), 524-535. https://doi.org/10.1016/j.ijpe.2006.02.013
Nie, P.-Y., Wang, C., Chen, Y.-H., & Yang, Y.-C. (2018). Effects of switching costs on innovative investment. Technological and Economic Development of Economy, 24(3), 933-949. https://doi.org/10.3846/tede.2018.1430
Nie, P.-Y., & Wang, C. (2019). An analysis of cost-reduction innovation under capacity constrained inputs. Applied Economics, 51(6), 564-576. https://doi.org/10.1080/00036846.2018.1497850
Okamuro, H., & Nishimura, J. (2018). Whose business is your project? A comparative study of different subsidy policy schemes for collaborative R&D. Technological Forecasting and Social Change, 127, 85-96. https://doi.org/10.1016/j.techfore.2017.07.017
Papineau, M. (2006). An economic perspective on experience curves and dynamic economies in renewable energy technologies. Energy Policy, 34(4), 422-432. https://doi.org/10.1016/j.enpol.2004.06.008
Patibandla, M., & Petersen, B. (2002). Role of transnational corporations in the evolution of a high-tech industry: the case of India’s software industry. World Development, 30(9), 1561-1577. https://doi.org/10.1016/S0305-750X(02)00060-8
Peters, T., & Waterman, R. (1985). In search of excellence: Lessons from America’s best-run companies. Journal of Business Ethics, 4(1), 70-80.
Pellegrino, R., Costantino, N., Pietroforte, R., & Sancilio, S. (2012). Construction of multi-storey concrete structures in Italy: patterns of productivity and learning curves. Construction Management and Economics, 30(2), 103-115. https://doi.org/10.1080/01446193.2012.660776
Söderblom, A., Samuelsson, M., Wiklund, J., & Sandberg, R. (2015). Inside the black box of outcome additionality: effects of early-stage government subsidies on resource accumulation and new venture performance. Research Policy, 44(8), 1501-1512. https://doi.org/10.1016/j.respol.2015.05.009
Söderholm, P., & Sundqvist, T. (2007). Empirical challenges in the use of learning curves for assessing the economic prospects of renewable energy technologies. Renewable Energy, 32(15), 2559-2578. https://doi.org/10.1016/j.renene.2006.12.007
Sohn, S. Y., & Moon, T. H. (2004). Decision tree based on data envelopment analysis for effective technology commercialization. Expert Systems with Applications, 26(2), 279-284. https://doi.org/10.1016/j.eswa.2003.09.011
Wang, C., Nie, P.-Y., Peng, D.-H., & Li, Z.-H. (2017). Green insurance subsidy for promoting clean production innovation. Journal of Cleaner Production, 148, 111-117. https://doi.org/10.1016/j.jclepro.2017.01.145
Wang, E. C., & Huang, W. (2007). Relative efficiency of R&D activities: a cross-country study accounting for environmental factors in the DEA approach. Research Policy, 36(2), 260-273. https://doi.org/10.1016/j.respol.2006.11.004
Wang, Y.-H., Lin, W.-R., Lin, S.-S., & Hung, J.-C. (2017). How does patent litigation influence dynamic risk for market competitors? Technological and Economic Development of Economy, 23(5), 780-793. https://doi.org/10.3846/20294913.2015.1074949
Wang, X., Li, H., Li, R., Li, B., & Zhu, D. (2016). Research on the cost forecast of China’s photovoltaic industry. R&D Management, 46(1), 3-12. https://doi.org/10.1111/radm.12102
Wong, C.-Y., & Govindaraju, V. C. (2012). Technology stocks and economic performance of government-linked companies: the case of Malaysia. Technological and Economic Development of Economy, 18(2), 248-261. https://doi.org/10.3846/20294913.2012.688313
Wright, T. P. (1936). Factors affecting the cost of airplanes. Journal of Aeronautical Sciences, 3(4), 122128. https://doi.org/10.2514/8.155
Yang, Y.-C., Nie, P.-Y., Liu, H.-T., & Shen, M.-H. (2018). On the welfare effects of subsidy game for renewable energy investment: toward a dynamic equilibrium model. Renewable Energy, 121, 420428. https://doi.org/10.1016/j.renene.2017.12.097
Barreto, L., & Kypreos, S. (2004). Endogenizing R&D and market experience in the “bottom-up” energysystems ERIS model. Technovation, 24(8), 615-629. https://doi.org/10.1016/S0166-4972(02)00124-4
Chen, Y.-H., Wen, X.-W., Wang, B., & Nie, P.-Y. (2017). Agricultural pollution and regulation: How to subsidize agriculture? Journal of Cleaner Production, 164, 258-264. https://doi.org/10.1016/j.jclepro.2017.06.216
Choi, J., & Yeniyurt, S. (2015). Contingency distance factors and international research and development (R&D), marketing, and manufacturing alliance formations. International Business Review, 24(6), 1061-1071. https://doi.org/10.1016/j.ibusrev.2015.04.007
Chung, S. (2001). The learning curve and the yield factor: the case of Korea’s semiconductor industry. Applied Economics, 33(4), 473-483. https://doi.org/10.1080/00036840122474
Coccia, M. (2009). What is the optimal rate of R&D investment to maximize productivity growth? Technological Forecasting and Social Change, 76(3), 433-446. https://doi.org/10.1016/j.techfore.2008.02.008
Czarnitzki, D., & Hussinger, K. (2018). Input and output additionality of R&D subsidies. Applied Economics, 50(12), 1324-1341. https://doi.org/10.1080/00036846.2017.1361010
Dodgson, M. (1991). The management of technological learning: lessons from a biotechnology company. Berlin: Walter de Gruyter. https://doi.org/10.1515/9783110867749
El-Mashaleh, M., Al-Smadi, B. M., Hyari, K. H., & Rababeh, S. M. (2010). Safety management in the Jordanian construction industry. Jordan Journal of Civil Engineering, 4(1), 117-120.
Grübler, A., & Messner, S. (1998). Technological change and the timing of mitigation measures. Energy Economics, 20(5), 495-512. https://doi.org/10.1016/S0140-9883(98)00010-3
Guan, J., Zuo, K., Chen, K., & Yam, R. C. M. (2016). Does country-level R&D efficiency benefit from the collaboration network structure? Research Policy, 45(4), 770-784. https://doi.org/10.1016/j.respol.2016.01.003
Haas, D. A., & Murphy, F. H. (2003). Compensating for non-homogeneity in decision-making units in data envelopment analysis. European Journal of Operational Research, 144(3), 530-544. https://doi.org/10.1016/S0377-2217(02)00139-X
Hall, B., & Van Reenen, J. (2000). How effective are fiscal incentives for R&D? A review of the evidence. Research Policy, 29(4), 449-469. https://doi.org/10.1016/S0048-7333(99)00085-2
Han, Y. J. (2007). Measuring industrial knowledge stocks with patents and papers. Journal of Informetrics, 1(4), 269-276. https://doi.org/10.1016/j.joi.2007.06.001
Hanna, N., Boyson, S., & Gunaratne, S. (1999). The east Asian miracle and information technology: strategic management of technological learning. Washington: World Bank Publications.
Hashimoto, A., & Haneda, S. (2008). Measuring the change in R&D efficiency of the Japanese pharmaceutical industry. Research Policy, 37(10), 1829-1836. https://doi.org/10.1016/j.respol.2008.08.004
Hitt, M. A., Ireland, R. D., & Lee, H. U. (2000). Technological learning, knowledge management, firm growth and performance: an introductory essay. Journal of Engineering and Technology Management, 17(3), 231-246. https://doi.org/10.1016/S0923-4748(00)00024-2
Ho, L. A., & Kuo, T. H. (2010). How can one amplify the effect of e-learning? An examination of hightech employees’ computer attitude and flow experience. Computers in Human Behavior, 26(1), 2331. https://doi.org/10.1016/j.chb.2009.07.007
Hsu, C. W., & Chiang, H. C. (2001). The government strategy for the upgrading of industrial technology in Taiwan. Technovation, 21(2), 123-132. https://doi.org/10.1016/S0166-4972(00)00029-8
Hsu, F. M., & Hsueh, C. C. (2009). Measuring relative efficiency of government-sponsored R&D projects: a three-stage approach. Evaluation and Program Planning, 32(2), 178-186. https://doi.org/10.1016/j.evalprogplan.2008.10.005
Hudon, M., & Traca, D. (2011). On the efficiency effects of subsidies in microfinance: an empirical inquiry. World Development, 39(6), 966-973. https://doi.org/10.1016/j.worlddev.2009.10.017
Kelm, K. M., Narayanan, V., & Pinches, G. E. (1995). Shareholder value creation during R&D innovation and commercialization stages. Academy of Management Journal, 38(3), 770-786. https://doi.org/10.2307/256745
Kessler, E. H., Bierly, P. E., & Gopalakrishnan, S. (2000). Internal vs. external learning in new product development: effects on speed, costs and competitive advantage. R&D Management, 30(3), 213-224. https://doi.org/10.1111/1467-9310.00172
Kouvaritakis, N., Soria, A., & Isoard, S. (2000). Modelling energy technology dynamics: methodology for adaptive expectations models with learning by doing and learning by searching. International Journal of Global Energy Issues, 14(1-4), 104-115. https://doi.org/10.1504/IJGEI.2000.004384
Lall, S. (2000). The technological structure and performance of developing country manufactured exports, 1985‐98. Oxford Development Studies, 28(3), 337-369. https://doi.org/10.1080/713688318
Lev, B., Radhakrishnan, S., & Zhang, W. (2009). Organization capital. Abacus, 45(3), 275-298. https://doi.org/10.1111/j.1467-6281.2009.00289.x
Li, N. (2014). Labor market peer firms. University of Chicago, The University of Chicago Booth School of Business.
Li, N. (2017, November 1). Who are my peers? Labor market peer firms through employees’ internet co-search patterns (Rotman School of Management Working Paper No. 2558271). Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2558271
Linton, J. D., Walsh, S. T., & Morabito, J. (2002). Analysis, ranking and selection of R&D projects in a portfolio. R&D Management, 32(2), 139-148. https://doi.org/10.1111/1467-9310.00246
Lo, S.-F., & Lu, W.-M. (2009). An integrated performance evaluation of financial holding companies in Taiwan. European Journal of Operational Research, 198(1), 341-350. https://doi.org/10.1016/j.ejor.2008.09.006
Lyu, K., Bian, Y., & Yu, A. (2018). Environmental efficiency evaluation of industrial sector in China by incorporating learning effects. Journal of Cleaner Production, 172, 2464-2474. https://doi.org/10.1016/j.jclepro.2017.11.163
Moorman, C., Wies, S., Mizik, N., & Spencer, F. J. (2012). Firm innovation and the ratchet effect among consumer packaged goods firms. Marketing Science, 31(6), 934-951. https://doi.org/10.1287/mksc.1120.0737
Nakata, T., Sato, T., Wang, H., Kusunoki, T., & Furubayashi, T. (2011). Modeling technological learning and its application for clean coal technologies in Japan. Applied Energy, 88(1), 330-336. https://doi.org/10.1016/j.apenergy.2010.05.022
Ngwenyama, O., Guergachi, A., & McLaren, T. (2007). Using the learning curve to maximize IT productivity: a decision analysis model for timing software upgrades. International Journal of Production Economics, 105(2), 524-535. https://doi.org/10.1016/j.ijpe.2006.02.013
Nie, P.-Y., Wang, C., Chen, Y.-H., & Yang, Y.-C. (2018). Effects of switching costs on innovative investment. Technological and Economic Development of Economy, 24(3), 933-949. https://doi.org/10.3846/tede.2018.1430
Nie, P.-Y., & Wang, C. (2019). An analysis of cost-reduction innovation under capacity constrained inputs. Applied Economics, 51(6), 564-576. https://doi.org/10.1080/00036846.2018.1497850
Okamuro, H., & Nishimura, J. (2018). Whose business is your project? A comparative study of different subsidy policy schemes for collaborative R&D. Technological Forecasting and Social Change, 127, 85-96. https://doi.org/10.1016/j.techfore.2017.07.017
Papineau, M. (2006). An economic perspective on experience curves and dynamic economies in renewable energy technologies. Energy Policy, 34(4), 422-432. https://doi.org/10.1016/j.enpol.2004.06.008
Patibandla, M., & Petersen, B. (2002). Role of transnational corporations in the evolution of a high-tech industry: the case of India’s software industry. World Development, 30(9), 1561-1577. https://doi.org/10.1016/S0305-750X(02)00060-8
Peters, T., & Waterman, R. (1985). In search of excellence: Lessons from America’s best-run companies. Journal of Business Ethics, 4(1), 70-80.
Pellegrino, R., Costantino, N., Pietroforte, R., & Sancilio, S. (2012). Construction of multi-storey concrete structures in Italy: patterns of productivity and learning curves. Construction Management and Economics, 30(2), 103-115. https://doi.org/10.1080/01446193.2012.660776
Söderblom, A., Samuelsson, M., Wiklund, J., & Sandberg, R. (2015). Inside the black box of outcome additionality: effects of early-stage government subsidies on resource accumulation and new venture performance. Research Policy, 44(8), 1501-1512. https://doi.org/10.1016/j.respol.2015.05.009
Söderholm, P., & Sundqvist, T. (2007). Empirical challenges in the use of learning curves for assessing the economic prospects of renewable energy technologies. Renewable Energy, 32(15), 2559-2578. https://doi.org/10.1016/j.renene.2006.12.007
Sohn, S. Y., & Moon, T. H. (2004). Decision tree based on data envelopment analysis for effective technology commercialization. Expert Systems with Applications, 26(2), 279-284. https://doi.org/10.1016/j.eswa.2003.09.011
Wang, C., Nie, P.-Y., Peng, D.-H., & Li, Z.-H. (2017). Green insurance subsidy for promoting clean production innovation. Journal of Cleaner Production, 148, 111-117. https://doi.org/10.1016/j.jclepro.2017.01.145
Wang, E. C., & Huang, W. (2007). Relative efficiency of R&D activities: a cross-country study accounting for environmental factors in the DEA approach. Research Policy, 36(2), 260-273. https://doi.org/10.1016/j.respol.2006.11.004
Wang, Y.-H., Lin, W.-R., Lin, S.-S., & Hung, J.-C. (2017). How does patent litigation influence dynamic risk for market competitors? Technological and Economic Development of Economy, 23(5), 780-793. https://doi.org/10.3846/20294913.2015.1074949
Wang, X., Li, H., Li, R., Li, B., & Zhu, D. (2016). Research on the cost forecast of China’s photovoltaic industry. R&D Management, 46(1), 3-12. https://doi.org/10.1111/radm.12102
Wong, C.-Y., & Govindaraju, V. C. (2012). Technology stocks and economic performance of government-linked companies: the case of Malaysia. Technological and Economic Development of Economy, 18(2), 248-261. https://doi.org/10.3846/20294913.2012.688313
Wright, T. P. (1936). Factors affecting the cost of airplanes. Journal of Aeronautical Sciences, 3(4), 122128. https://doi.org/10.2514/8.155
Yang, Y.-C., Nie, P.-Y., Liu, H.-T., & Shen, M.-H. (2018). On the welfare effects of subsidy game for renewable energy investment: toward a dynamic equilibrium model. Renewable Energy, 121, 420428. https://doi.org/10.1016/j.renene.2017.12.097