Keywords
stable ICT combinations
industry
artificial intelligence
productivity
profitability
How to Cite
Abstract
A productive approach to analysing the Russian information and communication technologies landscape is focusing on commonly used industry-specific stable ICT combinations. At the corporate level, such combinations reflect companies’ ICT profiles, which the resource theory defines as the firm’s ability to create competitive advantages by making use of resource complementarity. Unlike previous studies, the authors didn’t use ICT combinations suggested by experts, but applied a specially designed tool based on machine learning techniques to automatically search for interconnected ICTs, which allowed to identify stable technology combinations applied by numerous companies in various industries.
ICT profiles were identified by analysing the relationships between a wide variety of relevant technologies ranging from basic infrastructure to AI-based business performance management systems. The final dataset included 110 ICTs applied by over 29,000 companies in 31 industries in 2006-2022. The analysis allowed to draw the following conclusions: (1) typical for most industries profile comprises a combination of BPM and SaaS systems; (2) insurance and finance have the highest diversity and complexity of ICT profiles; (3) supplementing ICT profiles with AI-based solutions holds great potential for Russian companies; (4) implementing ICT profiles affects companies' financial performance; albeit quite differently in different industries.
References
Agarwal G.K., Magnusson M., Johanson A. (2021) Edge AI Driven Technology Advancements Paving Way Towards New Capabilities. International Journal of Innovation and Technology Management, 18(01), 2040005. DOI: https://doi.org/10.1142/S0219877020400052
Agrawal R., Imieliński T., Swami A. (1993) Mining association rules between sets of items in large databases. Paper presented at the 1993 ACM SIGMOD International Conference on Management of Data, June 1993. DOI: https://doi.org/10.1145/170035.170072
Agrawal R., Srikant R., Road H., Jose S. (1994) Fast Algorithms for Mining Association Rules. Paper presented at the 20th International Conference on Very Large Data Bases (VLDB), September 12-15, 1994, Santiago de Chile, Chile.
Amid A., Moalagh M., Zare Ravasan A. (2012) Identification and classification of ERP critical failure factors in Iranian industries. Information Systems, 37(3), 227-237. DOI: https://doi.org/10.1016/j.is.2011.10.010
Barney J., Wright M., Ketchen D.J. (2001) The resource-based view of the firm: Ten years after 1991. Journal of Management, 27(6), 625-641. DOI: https://doi.org/10.1177/014920630102700601
Batarseh F.A., Gopinath M., Monken A., Gu Z. (2021) Public policymaking for international agricultural trade using association rules and ensemble machine learning. Machine Learning with Applications, 5, 100046. DOI: https://doi.org/10.1016/j.mlwa.2021.100046
Bellemare M.F., Masaki T., Pepinsky T.B. (2017) Lagged Explanatory Variables and the Estimation of Causal Effect. The Journal of Politics, 79(3), 949-963. DOI: https://doi.org/10.1086/690946
Chae H.-C., Koh C.E., Prybutok V.R. (2014) Information Technology Capability and Firm Performance: Contradictory Findings and Their Possible Causes. MIS Quarterly, 38(1), 305-326. DOI: https://doi.org/10.25300/MISQ/2014/38.1.14
Chae H.-C., Koh C.E., Park K.O. (2018) Information technology capability and firm performance: Role of industry. Information & Management, 55(5), 525-546. DOI: https://doi.org/10.1016/j.im.2017.10.001
Coşkun E., Gezici B., Aydos M., Tarhan A.K., Garousi V. (2022) ERP failure: A systematic mapping of the literature. Data & Knowledge Engineering, 142, 102090. DOI: https://doi.org/10.1016/j.datak.2022.102090
Daviy A. (2022) Does the regional environment matter in ERP system adoption? Evidence from Russia. Journal of Enterprise Information Management, 36(2), 437-458. DOI: https://doi.org/10.1108/JEIM-11-2021-0488
Devece C., Palacios-Marqués D., Galindo-Martín M.-Á., Llopis-Albert C. (2017) Information Systems Strategy and its Relationship with Innovation Differentiation and Organizational Performance. Information Systems Management, 34(3), 250-264. DOI: https://doi.org/10.1080/10580530.2017.1330002
Díaz-Chao Á., Ficapal-Cusí P., Torrent-Sellens J. (2021) Environmental assets, Industry 4.0 technologies and firm performance in Spain: A dynamic capabilities path to reward sustainability. Journal of Cleaner Production, 281, 125264. DOI: https://doi.org/10.1016/j.jclepro.2020.125264
Dumas M., Fournier F., Limonad L., Marrella A., Montali M., Rehse J.-R., Accorsi R., Calvanese D., De Giacomo G., Fahland D., Gal A., La Rosa M., Völzer H., Weber I. (2023) AI-Augmented Business Process Management Systems: A Research Manifesto. ACM Transactions on Management Information Systems, 14(1), 1-19. DOI: https://doi.org/10.1145/3576047
Enholm I.M., Papagiannidis E., Mikalef P., Krogstie J. (2022) Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24(5), 1709-1734. DOI: https://doi.org/10.1007/s10796-021-10186-w
Enekwe C., Agu C., Nnagbogu E. (2014) The Effect of Financial Leverage on Financial Performance: Evidence of Quoted Pharmaceutical Companies in Nigeria. IOSR Journal of Economics and Finance, 5(3), 17-25. DOI: https://doi.org/10.9790/5933-0531725
Geum Y., Lee H., Lee Y., Park Y. (2015) Development of data-driven technology roadmap considering dependency: An ARM-based technology roadmapping. Technological Forecasting and Social Change, 91, 264-279. DOI: https://doi.org/10.1016/j.techfore.2014.03.003
Gupta S., Qian X., Bhushan B., Luo Z. (2019) Role of cloud ERP and big data on firm performance: A dynamic capability view theory perspective. Management Decision, 57(8), 8. DOI: https://doi.org/10.1108/MD-06-2018-0633
Habibi F., Zabardast M.A. (2020) Digitalization, education and economic growth: A comparative analysis of Middle East and OECD countries. Technology in Society, 63, 101370. DOI: https://doi.org/10.1016/j.techsoc.2020.101370
HassabElnaby H.R., Hwang W., Vonderembse M.A. (2012) The impact of ERP implementation on organizational capabilities and firm performance. Benchmarking: An International Journal, 19(4/5), 618-633. DOI: https://doi.org/10.1108/14635771211258043
Hegland M. (2003) Algorithms for Association Rules. In: Advanced Lectures on Machine Learning: Machine Learning Summer School 2002 Canberra, Australia, February 11-22, 2002 Revised Lectures (eds. S. Mendelson, A.J. Smola), Heidelberg, Dordrecht, London, New York: Springer, pp. 226-234. DOI: https://doi.org/10.1007/3-540-36434-X_7
Hendricks K.B., Singhal V.R., Stratman J.K. (2007) The impact of enterprise systems on corporate performance: A study of ERP, SCM, and CRM system implementations. Journal of Operations Management, 25(1), 65-82. DOI: https://doi.org/10.1016/j.jom.2006.02.002
Hikmawati E., Maulidevi N.U., Surendro K. (2021) Minimum threshold determination method based on dataset characteristics in association rule mining. Journal of Big Data, 8(1), 146. DOI: https://doi.org/10.1186/s40537-021-00538-3
Hill A.D., Johnson S.G., Greco L.M., Walter S.L. (2021) Endogeneity: A Review and Agenda for the Methodology-Practice Divide Affecting Micro and Macro Research. Journal of Management, 47(1), 105-143. DOI: https://doi.org/10.1177/0149206320960533
Jacobsson M., Linderoth H.C.J., Rowlinson S. (2017) The role of industry: An analytical framework to understand ICT transformation within the AEC industry. Construction Management and Economics, 35(10), 611-626. DOI: https://doi.org/10.1080/01446193.2017.1315148
Karim M.S., Nahar S., Demirbag M. (2022) Resource-Based Perspective on ICT Use and Firm Performance: A Meta-analysis Investigating the Moderating Role of Cross-Country ICT Development Status. Technological Forecasting and Social Change, 179, 121626. DOI: https://doi.org/10.1016/j.techfore.2022.121626
Kaur J., Dharni K. (2022) Assessing efficacy of association rules for predicting global stock indices. DECISION, 49(3), 329-339. DOI: https://doi.org/10.1007/s40622-022-00327-8
Kotsiantis S., Kanellopoulos D. (2006) Association rules mining: A recent overview. GESTS International Transactions on Computer Science and Engineering, 32(1), 71-82.
Li L., Tong Y., Wei L., Yang S. (2022) Digital technology-enabled dynamic capabilities and their impacts on firm performance: Evidence from the COVID-19 pandemic. Information & Management, 59(8), 103689. DOI: https://doi.org/10.1016/j.im.2022.103689
Mithas S., Rust R.T. (2016) How Information Technology Strategy and Investments Influence Firm Performance: Conjecture and Empirical Evidence. MIS Quarterly, 40(1), 223-245. DOI: https://doi.org/10.25300/MISQ/2016/40.1.10
Oh I., Kim J. (2023) Frontiers and laggards: Which firms benefit from adopting advanced digital technologies? Managerial and Decision Economics, 44(2), 753-766. DOI: https://doi.org/10.1002/mde.3710
Shakina E., Naidenova I., Barajas A. (2022) Shadow prices for intangible resources. Journal of Intellectual Capital, 23(3), 666-686. DOI: https://doi.org/10.1108/JIC-02-2020-0031
Teece D.J. (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: https://doi.org/10.1016/j.respol.2017.01.015
Wu Y.-C.J., Dong T.-P., Chang C.-L., Liao Y.-C. (2015) A collaborative learning lesson from using effective information technology combinations. Computers in Human Behavior, 51, 986-993. DOI: https://doi.org/10.1016/j.chb.2014.10.008
Xue Y., Liang H., Boulton W.R., Snyder C.A. (2005) ERP implementation failures in China: Case studies with implications for ERP vendors. International Journal of Production Economics, 97(3), 279-295. DOI: https://doi.org/10.1016/j.ijpe.2004.07.008
Zavertiaeva M.A., López-Iturriaga F.J., López-Iturriaga F.J. (2020) Networks of directors on Russian boards: The hidden part of the corporate governance iceberg. Russian Management Journal, 18(1), 29-50. DOI: https://doi.org/10.21638/spbu18.2020.102
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