Drivers of Advanced Digital Technologies
Main Article Content
Abstract
Advancements in digital technologies, especially computer and information systems, force firms to adopt them in their production processes. Artificial intelligence, cloud computing, big data analytics, robotics, smart devices, and blockchain are the leading advanced technologies. This study explores the drivers of firms’ adoption of these technologies by estimating a multivariate probit model utilizing a Eurobarometer dataset. A statistically significant and positive correlation between the error terms of all models indicates that investigating the adoption of all advanced digital technologies together is more appropriate than independent analyses. Drivers of advanced digital technologies appear similar with decisive factors in using new technologies, and implementation of any type of innovation significantly increases the probability of adoption. The other determinants are the firm size, interaction with international markets, and the network structure of the market in which firms operate. Furthermore, location positively impacts the adoption of cloud computing and big data analytics, while it exerts no significant influence on the adoption of other types of advanced digital technologies.
JEL Codes: D22, D83, 033
Keywords: Digital technology, Technology diffusion, Multivariate probit model
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How to Cite
Fikirli, Özlem and Şahin, H. (2024) “Drivers of Advanced Digital Technologies”, World Journal of Applied Economics, 10(2), pp. 43-54. doi: 10.22440/wjae.10.2.1.
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Research Articles
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References
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Tornatzky, L., & Fleischer, M. (1990). The process of technology innovation. D.C. Heath & Company, Lexington, MA. doi:10.1007/BF02371446
Wooldridge, J. M. (2007). Econometric Analysis of Cross Section and Panel Data. The MIT Press.
Xu, D., Guo, Y., & Huang, M. (2021). Can artificial intelligence improve firms’ competitiveness during the COVID-19 pandemic: international evidence. Emerging Markets Finance and Trade, 57 (10), 2812-2825. doi:10.1080/1540496X.2021.1899911
Young, H. P. (2009). Innovation diffusion in heterogeneous populations: Contagion, social influence, and social learning. American Economic Review, 99 (5), 1899-1924. doi:10.1257/aer.99.5.1899
Zolas, N., Kroff, Z., Brynjolfsson, E., McElheran, K., Beede, D. N., Buffington, C., . . . Dinlersoz, E. (2021). Advanced technologies adoption and use by US firms: Evidence from the annual business survey (Working Papers No. 28290). NBER. doi:10.3386/w28290
Arvanitis, S., Kyriakou, N., & Loukis, E. N. (2017). Why do firms adopt cloud computing? A comparative analysis based on South and North Europe firm data. Telematics and Informatics, 34 (7), 1322-1332. doi:10.1016/j.tele.2016.05.013
Ballestar, M. T., Diaz-Chao, A., Sainz, J., & Torrent-Sellens, J. (2020). Knowledge, robots and productivity in SMEs: Explaining the second digital wave. Journal of Business Research, 108, 119-131. doi:10.1016/j.jbusres.2019.11.017
Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics: Methods and Applications. Cambridge University Press.
Cappellari, L., & Jenkins, S. P. (2003). Multivariate probit regression using simulated maximum likelihood. The STATA Journal, 3 (3), 278-294. doi:10.1177/1536867X0300300305
Chatzoglou, P., & Chatzoudes, D. (2016). Factors affecting e-business adoption in SMEs: An empirical research. Journal of Enterprise Information Management, 29 (3), 327-358. doi:10.1108/JEIM-03-2014-0033
De Caria, R. (2017). A digital revolution in international trade? The international legal framework for blockchain technologies, virtual currencies and smart contracts: challenges and opportunities. In Modernizing International Trade Law to Support Innovation and Sustainable Development. Proceedings of the Congress of the United Nations Commission on International Trade Law. Vienna, 4-6 July 2017 (p. 105-117).
Denicolai, S., Zucchella, A., & Magnani, G. (2021). Internationalization, digitalization, and sustainability: Are SMEs ready? A survey on synergies and substituting effects among growth paths. Technological Forecasting and Social Change, 166, 120650. doi:10.1016/j.techfore.2021.120650
DeStefano, T., & Timmis, J. (2024). Robots and export quality. Journal of Development Economics, 168, 103248. doi:10.1016/j.jdeveco.2023.103248
Eurostat. (2024). https://ec.europa.eu/eurostat/data/database.
Ferraris, A., Mazzoleni, A., Devalle, A., & Couturier, J. (2019). Big data analytics capabilities and knowledge management: impact on firm performance. Management Decision, 57 (8), 1923-1936. doi:10.1108/MD-07-2018-0825
Giotopoulos, I., Kontolaimou, A., Korra, E., & Tsakanikas, A. (2017). What drives ICT adoption by SMEs? Evidence from a large-scale survey in Greece. Journal of Business Research, 81, 60-69. doi:10.1016/j.jbusres.2017.08.007
Greene, W. H. (2003). Econometric analysis (5th ed.). Pearson Education, New Jersey.
Kossai, M., de Souza, M. L. L., Zaied, Y. B., & Nguyen, P. (2020). Determinants of the adoption of information and communication technologies (ICTs): The case of Tunisian electrical and electronics sector. Journal of the Knowledge Economy, 11 (3), 845-864. doi:10.1007/s13132-018-0573-6
Kromann, L., Malchow-Moller, N., Skaksen, J. R., & Sorensen, A. (2020). Automation and productivity—a cross-country, cross-industry comparison. Industrial and Corporate Change, 29 (2), 265-287. doi:10.1093/icc/dtz039
Kshetri, N. (2011). Cloud Computing in the Global South: drivers, effects and policy measures. Third World Quarterly, 32 (6), 997-1014. doi:10.1080/01436597.2011.586225
Loukis, E., Arvanitis, S., & Kyriakou, N. (2017). An empirical investigation of the effects of firm characteristics on the propensity to adopt cloud computing. Information Systems and e-Business Management, 15, 963-988. doi:10.1007/s10257-017-0338-y
Masayuki, M. (2016). The Effects of Artificial Intelligence and Robotics on Business and Employment: Evidence from a survey on Japanese firms (Discussion Paper Series No. 16-E-066). Research Institute of Economy, Trade and Industry (RIETI). https://www.rieti.go.jp/jp/publications/dp/16e066.pdf.
Molinillo, S., & Japutra, A. (2017). Organizational adoption of digital information and technology: a theoretical review. The Bottom Line, 30 (1), 33-46. doi:10.1108/BL-01-2017-0002
Nicholas-Donald, A., Mahmood, M. A., & Trevino, L. L. (2018). Does adoption of cloud computing matter? The economic worth of cloud computing implementation. International Journal of Information Systems and Management, 1 (4), 328-342. doi:10.1504/IJISAM.2018.094756
Nicoletti, G., von Rueden, C., & Andrews, D. (2020). Digital technology diffusion: A matter of capabilities, incentives or both? European Economic Review, 128, 103513. doi:10.1016/j.euroecorev.2020.103513
Ohnemus, J., & Niebel, T. (2016). On the determinants of cloud computing adoption. In 27th European Regional Conference of the International Telecommunications Society (ITS): “The Evolution of the North-South Telecommunications Divide: The Role for Europe”. https://hdl.handle.net/10419/148694.
Pellegrini, L., Campi, S., Locatelli, M., Pattini, G., Di Giuda, G. M., & Tagliabue, L. C. (2021). Corrigendum: Digital Transition and Waste Management in Architecture, Engineering, Construction, and Operations Industry. Frontiers in Energy Research, 9 , 634357. doi:10.3389/fenrg.2021.634357
Rammer, C., Czarnitzki, D., & Fern´andez, G. P. (2021). Artificial intelligence and industrial innovation: Evidence from firm-level data (Discussion Paper No. 21-036). ZEW – Centre for European Economic Research. doi:10.2139/ssrn.3829822
Rogers, E. M. (1995). Diffusion of innovations (5th ed.). Free Press, New York.
Tornatzky, L., & Fleischer, M. (1990). The process of technology innovation. D.C. Heath & Company, Lexington, MA. doi:10.1007/BF02371446
Wooldridge, J. M. (2007). Econometric Analysis of Cross Section and Panel Data. The MIT Press.
Xu, D., Guo, Y., & Huang, M. (2021). Can artificial intelligence improve firms’ competitiveness during the COVID-19 pandemic: international evidence. Emerging Markets Finance and Trade, 57 (10), 2812-2825. doi:10.1080/1540496X.2021.1899911
Young, H. P. (2009). Innovation diffusion in heterogeneous populations: Contagion, social influence, and social learning. American Economic Review, 99 (5), 1899-1924. doi:10.1257/aer.99.5.1899
Zolas, N., Kroff, Z., Brynjolfsson, E., McElheran, K., Beede, D. N., Buffington, C., . . . Dinlersoz, E. (2021). Advanced technologies adoption and use by US firms: Evidence from the annual business survey (Working Papers No. 28290). NBER. doi:10.3386/w28290