Intangible assets and the efficiency of manufacturing firms in the age of digitalisation: the Russian case

• Autorzy: Turovets Yulia

Identyfikatory

DOI

10.2478/emj-2021-0001

• Języki publikacji: EN

Abstrakty

EN

A wide consensus exists on the role of intangible assets in both developed and developing economies, especially now, with the new generation of information and communication technologies. Emerging economies generally demonstrate lower endowment with intangibles (Dutz et al., 2012), but follow the same positive patterns for long-run development. In Russia, the contribution of intangibles to growth is still modest, and its capacity to foster productivity has not been achieved. As previous studies showed, efficiency represents one of the main channels of total factor productivity growth. This paper studies the effects of intangibles on the efficiency of Russian manufacturing firms in 2009–2018. Considering the heterogeneity of sectors and firms, the stochastic frontier model is applied. In general, the impact of intangibles is positive but small and influenced by external shocks and structural features. The paper provides evidence on different contributions of intangibles to efficiency for hightech and low-tech firms and its change over time. It contributes to the strand of literature regarding the technical efficiency measurement on the microlevel. On the practical side, the paper suggests an analytical framework for differentiated policy mechanisms to drive investments in intangibles, which are essential for current digital transformation.

Słowa kluczowe

EN

intangible assets; technical efficiency; manufacturing; digitalisation

PL

wartości niematerialne; wydajność techniczna; firma produkcyjna; cyfryzacja

• Wydawca: Oficyna Wydawnicza Politechniki Białostockiej
• Czasopismo: Engineering Management in Production and Services
• Rocznik: 2021
• Tom: Vol. 13, no. 1
• Strony: 7--26
• Opis fizyczny: Bibliogr. 127 poz., tab., wykr.

Twórcy

autor

Turovets Yulia

• National Research University Higher School of Economics, Russia

• https://orcid.org/0000-0002-6336-1255

Bibliografia

• Aboal, D., & Tacsir, E. (2018). Innovation and productivity in services and manufacturing: the role of ICT. Industrial and Corporate Change, 28(2), 221-241. doi: 10.1093/icc/dtx030
• Adarov, A., & Stehrer, R. (2019). Tangible and Intangible Assets in the Growth Performance of the EU, Japan and the US. The Vienna Institute for International Economic Studies Research Report, 442, 1-44.
• Aghion, P., & Howitt, P. (2006). Appropriate growth policy: A unifying framework. Journal of the European Economic Association, 4(2-3), 269-314. doi:10.1162/ jeea.2006.4.2-3.269
• Aigner, D., Lovell, C., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21- 37.
• Añón Higón, D., Gómez, J., & Vargas, P. (2017). Complementarities in innovation strategy: do intangibles play a role in enhancing firm performance? Industrial and Corporate Change, 26(5), 865-886.
• Apokin, Yu., & Ipatova, I. (2017). Components of Total Factor Productivity of the Russian Economy with Respect to Other Countries of the World: The Role of Technical Efficiency. Studies on Russian Economic Development, 28(1), 15-21. doi: 10.1134/S1075700717010026
• Australia Prime Minister’s Industry 4.0 Taskforce. (2017). Industry 4.0 Testlabs in Australia Preparing for the Future. Retrieved from https://www.industry.gov.au/sites/default/files/July%202018/document/pdf/ industry-4.0-testlabs-report.pdf?acsf_files_redirect
• Ayvazyan, S., Afanasev, M., & Rudenko, V. (2012). Some issues of specification of three-factor models of the company’s production potential that take into account intellectual capital [Nekotorye voprosy specifikacii trekhfaktornyh modelej proizvodstvennogo potenciala kompanii, uchityvayushchih intellektual’nyj kapital]. Applied Econometrics, 27(3), 36-69.
• Basu, S., & Fernald, J. (2007). Information and Communications Technology as a General‐Purpose Technology: Evidence from US Industry Data. German Economic Review, 8, 146-173.
• Battese, G. E., & Coelli, T. J. (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics, 20, 325-332. doi: 10.1007/BF01205442
• Bessonova, E. V. (2018). Analysis of Russian firms’ TFP growth in 2009-2015. Voprosy Ekonomiki, 7, 96-118. doi: 10.32609/0042-8736-2018-7-96-118
• Bieńkowska, A. (2020). Controlling Effectiveness Model — empirical research results regarding the influence of control on organisational performance. Engineering Management in Production and Services, 12(3), 28- 42. doi: 10.2478/emj-2020-0017
• BMWi. (2019). Case study on the Mittelstand 4.0 Competence Centres, Germany: Case study contribution to the OECD TIP Digital and Open Innovation project. Retrieved from https://www.innovationpolicyplatform.org/www.innovationpolicyplatform.org/system/files/imce/SME4.0CompetenceCentres_Germany_TIPDigitalCaseStudy2019_1/index.pdf
• BMWi. (2020). Intelligent transport systems in the field of road transport. Retrieved from https://www.bmvi.de/EN/Topics/Digital-Matters/Intelligent-Transport-Systems/intelligent-transport-systems.html
• Bogliacino, F., & Pianta, M. (2016). The Pavitt Taxonomy, revisited: patterns of innovation in manufacturing and services. Economics & Politics, 33, 153-180. doi: 10.1007/s40888-016-0035-1
• Bonanno, G. (2016). ICT and R&D as inputs or efficiency determinants? Analysing Italian manufacturing firms (2007–2009). Eurasian Business Review, 6(3), 383-404.
• Bontempi, M. E., & Mairesse, J. (2015). Intangible capital and productivity at the firm level: A panel data assessment. Economics of Innovation and New Technology, 24, 22-51.
• Borisova, E., Peresetsky, A., & Polishchuk, L., (2010). Stochastic frontier in non-profit associations’ performance assessment (the case of homeowners’ associations). Applied Econometrics, 20(4), 75-101.
• Borras, S., & Edquist, Ch. (2013). The Choice of Innovation Policy Instruments. Papers in Innovation Studies 2013/04, Lund University. Retrieved fromhttps://charlesedquist.files.wordpress.com/2013/02/201304_borrasedquist-21.pdf
• Brasini, S., & Freo, M. (2012). The impact of information and communication technologies: an insight at micro-level on one Italian region. Economics of Innovation and New Technology, 21(2), 107-123. doi: 10.1080/10438599.2011.558175
• Bresnahan T., & Trajtenberg, M. (1995). General purpose technologies ‘Engines of growth’? Journal of Econometrics, 65(1), 83-108.
• Brock G., & Ogloblin, C. (2018). Russian 1998–2007 TFP decomposed: some inspiration emerging from inherited Soviet legacy. Economic Change and Restructuring, 51(2), 135-151.
• Brynjolfsson, E. (1993). The Productivity Paradox of Information Technology. Communications of the ACM, 36, 66-77. doi: 10.1145/163298.163309
• Brynjolfsson, E., Rock, D., & Syverson, C. (2017). Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics. The Economics of Artificial Intelligence: An Agenda, 23-57.
• Cardona, M., Kretschmer, T., & Strobel, T. (2013). ICT and productivity: conclusions from the empirical literature. Information Economics and Policy, 25(3), 109- 125. doi: 10.1016/j.infoecopol.2012.12.002
• Castiglione, C. (2012). Technical efficiency and ICT investment in Italian manufacturing firms. Applied Economics, 44(14), 1749-1763.
• Castiglione, C., & Infante, D. (2014). ICTs and timespan in technical efficiency gains. A stochastic frontier approach over a panel of Italian manufacturing firms. Economic Modelling, 41, 55-65. doi: 10.1016/j.econmod.2014.04.021
• Caudill, S., & Ford, J., (1993). Biases in frontier estimation due to heteroscedasticity. Economics Letters, 41(1), 17-20.
• Chang, B., Huang, T., & Kuo, C. (2015). A comparison of the technical efficiency of accounting firms among the US, China, and Taiwan under the framework of a stochastic metafrontier production function. Journal of Productivity Analysis, 44(3), 337- 349.
• Chappell, N., & Jaffe, A. (2016). Intangible Investment and Firm Performance. Review of Industrial Organization, 52, 509-559.
• Chen, C., & Krumwiede, D. (2017). What makes a manufacturing firm effective for service innovation? The role of intangible capital under strategic and environmental conditions. International Journal of Production Economics, 193, 113-122. doi: 10.1016/j. ijpe.2017.07.007
• Chun, N., & Ishaq, H. (2016). Intangible Investment and Changing Sources of Growth in Korea. Japanese Economic Review, 67(1), 50-76.
• Coelli, T., Rahman, S., & Thirtle, C. (2003). A stochastic frontier approach to total factor productivity measurement in Bangladesh crop agriculture, 1961–92. Journal of International Development, 15, 321-333. doi:10.1002/jid.975
• Corrado, C., Haskel, J., Jona-Lasinio, C., & Iommi, M. (2013). Innovation and intangible investment in Europe, Japan, and the United States. Oxford Review of Economic Policy, 23(2), 261-286.
• Corrado, C., Hulten, C., & Sichel, D. (2005). Measuring Capital and Technology: An Expanded Framework. In C. Corrado, J. Haltiwanger, & D. Sichel (Eds.), Measuring Capital in the New Economy (pp. 11-46). Chicago, USA: University of Chicago Press.
• Corrado, C., Hulten, C., & Sichel, D. (2009). Intangible Capital and U.S. Economic Growth. The Review of Income and Wealth, 55, 661-685.
• Crass, D., & Peters, B. (2014). Intangible assets and firmlevel productivity. Centre for European Research Discussion Paper, 14-120.
• Dal Borgo, M., Goodridge, P., Haskel, J., & Pesole, A. (2013). Productivity and Growth in UK Industries: An Intangible Investment Approach. Oxford Bulletin of Economics and Statistics, 75, 806-834.
• David, P. (1990). The Dynamo and the Computer: An Historical Perspective on the Modern Productivity Paradox. American Economic Review: Papers and Proceedings, 80, 355-361.
• de Rassenfosse, G. (2017). An assessment of how well we account for intangibles. Industrial and Corporate Change, 26(3), 517-534. doi: 10.1093/icc/dtw034
• Dedrick, J., Kraemer, K., & Shih, E. (2013). Information Technology and Productivity in Developed and Developing Countries. Journal of Management Information Systems, 30(1), 97-122. doi: 10.2753/MIS0742-1222300103
• Delbecque, V., Bounfour, A., & Barreneche, A. (2015). Intangibles and Value Creation at the Industrial Level: Delineating Their Complementarities. In A. Bounfour, & T. Miyagawa (Eds.), Intangibles, Market Failure and Innovation Performance (pp. 27-56). Cham, UK: Springer.
• Dezhina, I., Ponomarev, A., & Frolov, A. (2015). Advanced Manufacturing Technologies in Russia: Outlines of a New Policy. Foresight-Russia, 9(1), 20-31.
• Dutz, M., Kannebley, S., Scarpelli, M., & Sharma, S. (2012). Measuring Intangible Assets in an Emerging Market Economy: An Application to Brazil. Policy Research Working Paper, No. 6142. Washington, DC: World Bank. Retrieved from https://openknowledge.worldbank.org/handle/10986/11972
• Ejdys, J. (2020). Trust-Based Determinants of Future Intention to Use Technology. Foresight and STI Governance, 14(1), 60-68. doi: 10.17323/2500-2597.2020.1.60.68
• European Commission. (2018). ICT innovation vouchers scheme for regions. Retrieved from https://ec.europa.eu/digital-single-market/en/ict-innovation-vouchers-scheme-regions
• Farrell, M. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society, 120(3), 253-290.
• Federal Aviation Administration. (2018). UAS Integration Pilot Program. Retrieved from https://www.faa.gov/uas/programs_partnerships/integration_pilot_program/
• Fleisher, B. M., McGuire, W. H., Smith, A. N., & Zhou, M. (2015). Knowledge capital, innovation, and growth in China. Journal of Asian Economics, 39, 31-42.
• France NUM. (2020). France NUM. Retrieved from https:// www.francenum.gouv.fr/france-num
• Fukao, K., Miyagawa, T., Mukai, K., Shinoda, Y., & Tonogi, K. (2009). Intangible Investment In Japan: Measurement And Contribution To Economic Growth. Review of Income and Wealth, 55, 717-736. doi:10.1111/ j.1475-4991.2009.00345.x
• Furman, J., & Seamans, R. (2018). AI and the Economy. Innovation Policy and the Economy, 19, 161-191. doi: 10.2139/ssrn.3186591
• GAO. (2019). Advanced Manufacturing: Innovation Institutes Have Demonstrated Initial Accomplishments, but Challenges Remain in Measuring Performance and Ensuring Sustainability. Retrieved from https://www.gao.gov/assets/700/699310.pdf
• Gershman, M., Gokhberg, L., Kuznetsova, T., & Roud, V. (2018). Bridging S&T and innovation in Russia: A historical perspective. Technological Forecasting and Social Change, 133, 132-140.
• Gokhberg, L., Ditkovskiy, K., Evnevich, E., Kuznetsova, I., Martynova, S., Ratay, T., Rosovetskaya, L., & Fridlyanova, S. (2020). Indicators of Innovation in the Russian Federation: 2020: Data Book. Moscow, Russia: National Research University Higher School of Economics.
• Goldar, B., & Parida, Y. (2017). Intangible Capital and Firm Productivity: A Study of Indian Corporate Sector Firms. South Asia Economic Journal, 18, 246-275.
• Gómez, J., & Vargas, P. (2012). Intangible resources and technology adoption in manufacturing firms. Research Policy, 41(9), 1607-1619. doi: 10.1016/j.respol.2012.04.016
• GOV.UK. (2017). UK Industrial Strategy. Retrieved from https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/ file/672468/uk-industrial-strategy-internationalbrochure-single-pages.pdf
• Greene, W. (2008). A Stochastic Frontier Model with Correction for Sample Selection. NYU Working Paper No. 2451/26017. Retrieved from https://ssrn.com/abstract=1281901
• Hall, B., Lottie, F., & Mairesse, J. (2013). Evidence on the impact of R&D and ICT investments on innovation and productivity in Italian firms. Economics of Innovation and New Technology, 22(3), 300-328. doi: 10.1080/10438599.2012.708134
• Hatzius, J., & Dawsey, K. (2015). Doing the Sums on Productivity Paradox v2.0. Goldman Sachs U.S. Economics Analyst, 15(30).
• Heshmati, A., Kumbhakar, S. C., & Hjalmarsson, L. (1995). Efficiency of the Swedish pork industry: A farm level study using rotating panel data 1976–1988. European Journal of Operational Research, 80(3), 519-533.
• Ipatova, I. (2015). The dynamics of total factor productivity and its components: Russian plastic production. Applied Econometrics, 38(2), 21-40.
• Ipatova, I., & Peresetsky, A. (2013). Technical efficiency of Russian plastic and rubber production firms. Applied Econometrics, 32(4), 71-92.
• ISSEK HSE. (2020). Dynamics and prospects of IT-industry development [Dinamika i perspektivy razvitiya ITotrasli]. Retrieved from https://issek.hse.ru/mirror/ pubs/share/371960649.pdf
• ITU. (2017). Measuring the Information Society Report 2017. Retrieved from https://www.itu.int/net4/ITUD/idi/2017/index.html
• Kılıçaslan, Y., Sickles, R. C., Kayış, A. A., & Gürel, Y. Ü. (2017). Impact of ICT on the productivity of the firm: evidence from Turkish manufacturing. Journal of Productivity Analysis, 47(3), 277-289.
• Kleis, L., Chwelos, P., Ramirez, R., & Cockburn, I. (2012). Information Technology and Intangible Output: The Impact of IT Investment on Innovation Productivity. Information Systems Research, 23(1), 42-59.
• Kozłowska, J. (2020). Servitization of manufacturing: survey in the Polish machinery sector. Engineering Management in Production and Services, 12(1), 20-33. doi: 10.2478/emj-2020-0002
• KPMG (2012). Intangible assets and goodwill. Retrieved from https://assets.kpmg/content/dam/kpmg/ru/ pdf/2012/RAP-Comparison/ru-ru-ifrs-vs-russiangaap-2012-C3-03.pdf
• Krasnopeeva, N., Nazrullaeva, E., Peresetsky, A., & Shchetinin, E. (2016). To export or not to export? The link between the exporter status of a firm and its technical efficiency in Russia’s manufacturing sector. Voprosy Ekonomiki, 7, 123-146. doi: 10.32609/0042-8736-2016-7-123-146
• Kumbhakar, S. C., & Lovell, C. K. (2000). Stochastic production frontier. Cambridge, UK: Cambridge University Press.
• Kumbhakar, S. C., & Peresetsky, A. (2013). Cost efficiency of Kazakhstan and Russian banks: results from competing panel data models-super-1. Macroeconomics and Finance in Emerging Market Economies, 6(1), 88-113.
• Kumbhakar, S. C., & Sarkar, S. (2003). Deregulation, ownership and productivity growth in the banking industry: Evidence from India. Journal of Money Credit and Banking, 35(3), 403-424.
• Kumbhakar, S., & Fuss, D. (2000). Estimation and decomposition of productivity change when production is not efficient: a panel data approach. Econometric Reviews, 19(4), 312-320.
• Kumbhakar, S., Lien, G., & Hardaker, J. (2014). Technical efficiency in competing panel data models: a study of Norwegian grain farming. Journal of Productivity Analysis, 41(2), 321-337.
• Kumbhakar. S. C., Parmeter, C. F., & Zelenyuk, V. (2017). Stochastic Frontier Analysis: Foundations and Advances. Working Papers 2017-10, University of Miami, Department of Economics. Retrieved from https://www.bus.miami.edu/_assets/files/repec/ WP2017-10.pdf
• Li, Y. (2009). A firm-level panel-data approach to efficiency, total factor productivity, catch-up and innovation, and mobile telecommunications reform (1995-2007). ESRC Centre for Competition Policy, University of East Anglia CCP Working Paper 09-6. Retrieved from http://competitionpolicy.ac.uk/documents/107435/107587/1.114399!ccp09-6.pdf
• Malakhov, D., & Pilnik, N. (2013). Methods of Estimating of the Efficiency in Stochastic Frontier Models. Ekonomicheskii zhurnal VSE, 5, 660-686.
• Marrocu, E., Paci, R., & Pontis, M. (2012). Intangible capital and firms’ productivity. Industrial and Corporate Change, 21, 377-402.
• Mattsson, P., Månsson, J., & Greene, W. H. (2020) TFP change and its components for Swedish manufacturing firms during the 2008–2009 financial crisis. Journal of Productivity Analysis, 53, 79-93.
• Meeusen, W., & van den Broeck, J. (1977). Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error. International Economic Review, 18(2), 435-44.
• METI. (2017). Recipe and Tools for Supporting Smart Monozukuri Targeting Mid-ranking Companies and SMEs in the Manufacturing Industry Compiled. Retrieved from https://www.meti.go.jp/english/ press/2017/1010_005.html
• Mogilat, A., & Ipatova, I. (2016). Technical efficiency as a factor of Russian industrial companies’ risks of financial distress. Applied Econometrics, 42, 5-29.
• Molodchik, M. A., Jardon, C. M., & Bykova, A. A. (2019). The performance effect of intellectual capital in the Russian context: Industry vs company level. Journal of Intellectual Capital, 20(3), 335-354. doi: 10.1108/JIC-10-2018-0190
• Montresor, S., & Vezzani, A. (2016). Intangible investments and innovation propensity: Evidence from the Innobarometer 2013. Industry and Innovation, 23(4), 331-352.
• Nakamura, L. I. (2010). Intangible Assets and National Income Accounting. The Review of Income and Wealth, 56, 55-135.
• Nazarko, J., Ejdys, J., Gudanowska, A., Halicka, K., Kononiuk, A., Magruk, A., & Nazarko, L. (2020). Roadmapping in Regional Technology Foresight: A Contribution to Nanotechnology Development Strategy. IEEE Transactions on Engineering Management, 99, 1-16. doi: 10.1109/TEM.2020.3004549
• Nazarko, L. (2017). Future-Oriented Technology Assessment. Procedia Engineering, 182, 504-509. doi: 10.1016/j.proeng.2017.03.144
• Neirotti, P., Raguseo, E., & Paolucci, E. (2018). How SMEs develop ICT-based capabilities in response to their environment: Past evidence and implications for the uptake of the new ICT paradigm. Journal of Enterprise Information Management, 31(1),10-37.
• Next Generation Manufacturing Canada. (2018). Pilot Project Application Guide: Building World-Leading Advanced Manufacturing Capabilities in Canada. Retrieved from https://www.ngen.ca/hubfs/Documents/NGenPilotProjectApplicationGuide_EN_ v1.1.pdf?hsLang=en
• Nwaiwu, F., Duduci, M., Chromjakova, F., & Otekhile, C.- A. F. (2020). Industry 4.0 concepts within the Czech SME manufacturing sector: an empirical assessment of critical success factors. Business: Theory and Practice, 21(1), 58-70. doi: 10.3846/btp.2020.10712
• OECD. (2017). OECD Digital Economy Outlook 2017. Paris, France: OECD Publishing.
• OECD. (2019). Fostering Science and Innovation in the Digital Age. Retrieved from https://www.oecd.org/ going-digital/fostering-science-and-innovation.pdf
• Paklina, S., Molodchik, M., & Fernández-Jardón, C. (2017). Intangible-Intensive Strategies of Russian Companies. Higher School of Economics. Research Paper No. WP BRP 57/MAN/2017.
• Piekkola, H. (2020). Intangibles and innovation-labor-biased technical change. Journal of Intellectual Capital, 21 (5), 649-669.
• Piekkola, H. (2020). Intangibles and innovation-laborbiased technical change. Journal of Intellectual Capital, 21(5), 649-669. doi: 10.1108/JIC-10-2019-0241
• Pieri, F., Vecchi, M., & Venturini, F. (2018). Modelling the joint impact of R&D and ICT on productivity: A frontier analysis approach. Research Policy, 47(9), 1842-1852.
• Podmetina, D., Väätänenet, J., Torkkeli, M., & Smirnova, M. (2011). Open innovation in Russian firms: An empirical investigation of technology commercialisation and acquisition. International Journal of Business Innovation and Research, 5(3), 298-317.
• Polder, M., Bondt, H., & Leeuwen, G. (2018). Business dynamics, industry productivity growth, and the distribution of firm-level performance: evidence for the role of ICT using Dutch firm-level data. The Journal of Technology Transfer, 43(6), 1522-1541.
• Ramirez, P. G., & Hachiya, T. (2008). Measuring the contribution of intangibles to productivity growth: a disaggregated analysis of Japanese firms. Review of Pacific Basin Financial Markets and Policies, 11, 151-186.
• Rasel, F. (2017). ICT and global sourcing – evidence for German manufacturing and service firms. Economics of Innovation and New Technology, 26(7), 634-660. doi: 10.1080/10438599.2016.1267939
• Roth, F. (2019). Intangible Capital and Labour Productivity Growth: A Review of the Literature, Hamburg Discussion Papers in International Economics. Retrieved from https://www.econstor.eu/bitstream/10419/207163/1/hdpie-no04.pdf
• Rylková, Z., & Šebestová, J. (2019). Benchmarking of contributory organisations within the framework of technical efficiency. Engineering Management in Production and Services, 11(1), 80-91. doi: 10.2478/ emj-2019-0006
• Sabirianova, K., Svejnar, J., & Terrell, K. (2005). Distance to the Efficiency Frontier and Foreign Direct Investment Spillovers. Journal of the European Economic Association, 3(2-3): 576-586.
• Shahabadi, A., Kimiaei, F., & Arbab Afzali, M. (2018). The Evaluation of Impacts of Knowledge-Based Economy Factors on the Improvement of Total Factor Productivity (a Comparative Study of Emerging and G7 Economies). Journal of the Knowledge Economy, 9(3), 896-907.
• Shahiduzzaman, M., Kowalkiewicz, M., & Barrett, R. (2018). Digital dividends in the phase of falling productivity growth and implications for policy making. International Journal of Productivity and Performance Management, 67(6), 1016-1032. doi: 10.1108/IJPPM-02-2017-0050
• Shakina, E., & Barajas, A. (2016). Intangible-intensive profiles of companies: protection during the economic crisis of 2008-2009. Journal of Intellectual Capital, 17(4), 758-775. doi: 10.1108/JIC-02-2016-0029
• Shakina, E., & Molodchik, M. (2014). Intangible-driven value creation: supporting and obstructing factors. Measuring Business Excellence, 18(3), 87-100. doi: 10.1108/MBE-12-2013-0063
• Shao, W., & Lin, W. (2002). Technical efficiency analysis of information technology investments: a two-stage empirical investigation. Information & Management, 39(5), 391-401. doi: 10.1016/S0378-7206(01)00105-7
• Sharma, S., Sylwester, K., & Heru, M. (2007). Decomposition of total factor productivity growth in U.S. states. The Quarterly Review of Economics and Finance, 47(2), 215-241.
• Shchetynin, Y. (2015). Effects of imports on technical efficiency in Russian food industry. Applied Econometrics, 37(1), 27-42.
• Shchetynin, Y., & Nazrullaeva, Eu. (2012). Effects of fixed capital investments on technical efficiency in food industry. Applied Econometrics, 28(4), 63-84.
• Simachev Y., & Kuzyk, M. (2019). Industrial Development, Structural Changes, and Industrial Policy in Russia. In Exploring the Future of Russia’s Economy and Markets: Towards Sustainable Economic Development (pp. 69-106). Emerald Group Publishing Limited.
• Simachev, Y., Kuzyk, M., & Feygina, V. (2014). The nature of innovation channels at the micro level: evidence from Russian manufacturing firms. Journal of Chinese Economic and Business Studies, 12(2), 103-123.
• Solow, R. (1957). Technical Change and the Aggregate Production Function. The Review of Economics and Statistics, 39(3), 312-320.
• Soltysova, Z., & Bednar, S. (2015). Complexity management in terms of mass customized manufacturing. Polish Journal of Management Studies, 12(2), 139-149.
• Spiezia, V. (2011). Are ICT users more innovative?: an analysis of ICT-enabled innovation in OECD firms. OECD Journal: Economic Studies, 2011(1), 1-21.
• Sun, Z., & Li, J. (2017). The multifaceted role of information and communication technology in innovation: evidence from Chinese manufacturing firms. Asian Journal of Technology Innovation, 25(1), 168-183. doi: 10.1080/19761597.2017.1302559
• Tambe, P., & Hitt, L. M. (2014). Measuring Information Technology Spillovers. Information Systems Research, 25(1), 53-71. doi: 10.1287/isre.2013.0498
• Tang, J., & Wang, W. (2020). Technological frontier, technical efficiency and the post-2000 productivity slowdown in Canada. Structural Change and Economic Dynamics, 55, 12-25. doi: 10.1016/j.strueco.2020.06.003
• Teece, D. (2018). Profiting from innovation in the digital economy: Enabling technologies, standards, and licensing models in the wireless world. Research Policy, 47(8), 1367-1387. doi: 10.1016/j.respol.2017.01.015
• Thum-Thysen, A., Voigt, P., Bilbao-Osorio, B., Maier, C., & Ognyanova, D. (2017). Unlocking Investment in Intangible Assets European Union. Discussion Paper 047. Retrieved from https://ec.europa.eu/info/sites/info/files/economy-finance/dp047_en.pdf
• Van Ark, B., Hao, J. X., Corrado, C., & Hulten, C. (2009). Measuring intangible capital and its contribution to economic growth in Europe. European Investment Bank, 14, 62-93.
• Voskoboynikov, I. (2020). Structural change, expanding informality and labor productivity growth in Russia. Review of Income and Wealth, 66(2), 394-417. doi: 10.1111/roiw.12417
• Yang, S., Zhou, Y., & Song, L. (2018). Determinants of Intangible Investment and Its Impacts on Firms’ Productivity: Evidence from Chinese Private Manufacturing Firms. China & World Economy, 26, 1-26.
• Zemtsov, S., Barinova, V., & Semenova, R. (2019). The Risks of Digitalization and the Adaptation of Regional Labor Markets in Russia. Foresight and STI Governance, 13(2), 84-96. doi: 10.17323/2500-2597.2019.2.84.96

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).