Abstract
This study explores the heterogeneous patterns of companies in terms of their AI capabilities by analyzing various combinations of AI-specific resources. Drawing on the resource-based theory of the firm, we develop an analytical framework comprising two key dimensions: AI infrastructure and AI competencies, and employ two scores to quantify these dimensions. We apply this approach to a dataset of 215 companies and categorize them into four distinct groups: beginners, followers with strong AI-infrastructure, followers with strong AI-specific human resource, and leaders in terms of AI capabilities. Our analysis provides insights into the companies’ sectoral affiliation, size classes, fields of usage of AI, and make or buy decisions regarding the uptake of AI solutions. Our findings suggest that the manufacturing and construction industry had the highest proportion of beginner companies with low AI capabilities, while the services and IT industry had the largest share of leader companies with strong AI capabilities. The study also shows that companies with different levels of AI capabilities have distinct motives for adopting AI technologies, and leading companies are more likely to use AI for product innovation purposes. Overall, the study provides a comprehensive analysis of the various AI-specific resources that contribute to a company’s AI capabilities and sheds more light on configurations of AI-specific resources. Our analytical framework can help organizations better understand their AI capabilities and identify areas for improvement.
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References
Davenport, T.H., Ronanki, R.: Artificial intelligence for the real world. Harv. Bus. Rev. 96, 108–116 (2018)
McKinsey: The state of AI in 2022—and a half decade in review (2022). https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2022-and-a-half-decade-in-review
Fountaine, T., McCarthy, B., Saleh, T.: Building the AI-powered organization. Harv. Bus. Rev. 97, 62–73 (2019)
Ransbotham, S., Khodabandeh, S., Fehling, R., et al.: Winning with AI. MIT Sloan Management Review (2019)
Brynjolfsson, E., Rock, D., Syverson, C.: Artificial intelligence and the modern productivity paradox: a clash of expectations and statistics. In: The Economics of Artificial Intelligence: An Agenda, pp. 23–57. University of Chicago Press (2018)
Ambati, L.S., Narukonda, K., Bojja, G.R., et al.: Factors influencing the adoption of artificial intelligence in organizations–from an employee’s perspective (2020)
Chui, M., Malhotra, S.: AI Adoption Advances, But Foundational Barriers Remain. Mckinsey and Company, New York (2018)
Barney, J.B.: Firm resources and sustained competitive advantage. [s.n.], [S.l.] (1991)
Horvat, D., Moll, C., Weidner, N.: Why and how to implement strategic competence management in manufacturing SMEs?. Procedia Manuf. 39, 824–832 (2019)
Grant, R.M.: The Resource-Based Theory of Competitive Advantage: Implications for Strategy Formulation. California Management Review Reprint Series. California Management Review, University of California, Berkeley, CA (1991)
Makadok, R.: Toward a synthesis of the resource-based and dynamic-capability views of rent creation. Strateg. Manag. J. 22, 387–401 (2001)
Helfat, C.E., Peteraf, M.A.: The Dynamic Resource-Based View: Capability Lifecycles. SSRN, S.l (2003)
Conboy, K., Mikalef, P., Dennehy, D., et al.: Using business analytics to enhance dynamic capabilities in operations research: a case analysis and research agenda. Eur. J. Oper. Res. 281, 656–672 (2020)
Mikalef, P., Pappas, I.O., Krogstie, J., et al.: Big data analytics capabilities: a systematic literature review and research agenda. IseB 16, 547–578 (2018)
Mikalef, P., Pateli, A.: Information technology-enabled dynamic capabilities and their indirect effect on competitive performance: findings from PLS-SEM and fsQCA. J. Bus. Res. 70, 1–16 (2017)
Wamba, S.F., Gunasekaran, A., Akter, S., et al.: Big data analytics and firm performance: effects of dynamic capabilities. J. Bus. Res. (2017). https://doi.org/10.1016/j.jbusres.2016.08.009
Bharadwaj, A.S.: A resource-based perspective on information technology capability and firm performance: an empirical investigation. MIS Q. 24, 169–196 (2000)
Schryen, G.: Revisiting IS business value research: what we already know, what we still need to know, and how we can get there. Eur. J. Inf. Syst. 22, 139–169 (2013)
Melville, N., Kraemer, K., Gurbaxani, V.: Review: technology information an performance: organizational integrative model of IT business value. MIS Q 28, 283–322 (2004)
Zhang, M.J.: Information systems, strategic flexibility and firm performance: an empirical investigation. J. Eng. Tech. Manage. 22, 163–184 (2005)
Mikalef, P., Gupta, M.: Artificial intelligence capability: conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Inf. Manage. 58, 103434 (2021)
Amit, R., Schoemaker, P.J.H.: Strategic assets and organizational rent. Strateg. Manag. J. 14, 33–46 (1993)
Sirmon, D.G.: Resource Orchestration to Create Competitive Advantage: Breadth, Depth, and Life Cycle Effects. SAGE Publications, New York (2011)
Horvat, D., Dreher, C., Som, O.: How firms absorb external knowledge—modelling and managing the absorptive capacity process. Int. J. Innov. Manag. 23, 1950041 (2019)
Mata, F.J., Fuerst, WL, Barney JB (1995) Information technology and sustained competitive advantage: a resource-based analysis. MIS Q. 19, 487–505 (1995)
Benitez, J., Castillo, A., Llorens, J., et al.: IT-enabled knowledge ambidexterity and innovation performance in small US firms: the moderator role of social media capability. Inf. Manage. 55, 131–143 (2018)
Sjödin, D., Parida, V., Palmié, M., et al.: How AI capabilities enable business model innovation: scaling AI through co-evolutionary processes and feedback loops. J. Bus. Res. 134, 574–587 (2021)
Najdawi, A. (ed.): Assessing AI Readiness Across Organizations: The Case of UAE. IEEE (2020)
Heimberger, H., Horvat, D., Schultmann, F.: Assessing AI-readiness in production – a conceptual approach: conference proceedings. In: 26th International Conference of Production Research (ICPR) (2022)
Jöhnk, J., Weißert, M., Wyrtki, K.: Ready or not, AI comes—an interview study of organizational AI readiness factors. Bus. Inf. Syst. Eng. 63, 5–20 (2021)
Mikalef, P., Fjørtoft, S.O., Torvatn, H.Y.: Developing an artificial intelligence capability: a theoretical framework for business value. In: Abramowicz, W., Corchuelo, R. (eds.) BIS 2019. LNBIP, vol. 373, pp. 409–416. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-36691-9_34
Tornatzky, L.G., Fleischer, M.: The Processes of Technological Innovation. Issues in Organization and Management Series. Lexington Books, Lexington (1990)
Kinkel, S., Baumgartner, M., Cherubini, E.: Prerequisites for the adoption of AI technologies in manufacturing – evidence from a worldwide sample of manufacturing companies. Technovation 110, 102375 (2021)
Sambamurthy, V., Zmud, R.W.: IT management competency assessment: a tool for creating business value through IT (working paper). Financial Executives Research Foundation (1994)
Kogut, B., Zander, U.: Knowledge of the firm, combinative capabilities, and the replication of technology. Organ. Sci. 3, 383–397 (1992)
Dierickx, I., Cool, K.: Asset stock accumulation and sustainability of competitive advantage. Manage. Sci. 35, 1504–1511 (1989)
Grant, R.M.: Prospering in dynamically competitive environment: organizational capability as knowledge and strategy resources for the knowledge-based economy, Woburn (1999)
Baumgartner, M., Horvat, D., Kinkel, S.: Künstliche Intelligenz in der Arbeitswelt – Eine Analyse der Kompetenzbedarfe auf Unternehmensebene. In: Gesellschaft für Arbeitswissenschaft e.V. (ed) Nachhaltig Arbeiten und Lernen. GfA-Press, Sankt Augustin (2023)
Wang, N., Liang, H., Zhong, W., et al.: Resource structuring or capability building? An empirical study of the business value of information technology. J. Manag. Inf. Syst. 29, 325–367 (2012)
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Horvat, D., Baumgartner, M., Kinkel, S., Mikalef, P. (2023). Examining Heterogeneous Patterns of AI Capabilities. In: Alfnes, E., Romsdal, A., Strandhagen, J.O., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures. APMS 2023. IFIP Advances in Information and Communication Technology, vol 690. Springer, Cham. https://doi.org/10.1007/978-3-031-43666-6_42
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