Innovation Policy and Artificial Intelligence in the Business and Economic Transformation of the European Freight Transport Industry

Amalia-Elena Ion () and Denisa-Atena Costovici ()
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Amalia-Elena Ion: Valahia University of Targoviste, Romania
Denisa-Atena Costovici: Valahia University of Targoviste, Romania

Chapter 13 in International Conference Innovative Business Management & Global Entrepreneurship (IBMAGE 2020), 2020, vol. 14, pp 168-186 from Editura Lumen

Abstract: A knowledge-intensive company is one that employs 20% of its workforce in research and development, having to manage the unique and intellectual property of the company. The latter is part of a growth-oriented business culture, basing its operations on innovation, a factor that permits a series of advantages, including tax reliefs. Moreover, the business would, especially in the actual economic conditions, has to make use of artificial intelligence, with the scope of analysing data, employing learning algorithms for efficiency and effectiveness of operations and strategy, and predicting patterns, correlations and, ultimately, developing models and policy functions. Although, there are some constraints in the world of knowledge-intensive services, especially that of the freight transport industry, as well as in the usage and implementation of AI, consisting of the lack or limited availability of data, infrastructure limitations, data retrieval capacity, computer machine learning software etc., the advantages of using AI in the problem-solving operations of knowledge enterprises determines a client-oriented approach, the strategic concentration on the problem-solving and innovation system creation, with the simple utilization of knowledge for the generation of tangible and intangible values. The research question of the present paper collides between those concepts, and develops on the proposition of a model for the intensive usage of AI in the knowledge-intensive freight transport industry and the related policy decision-making. The article includes a regression analysis on a World Bank database correlating the logistics performance, air freight transport, and railway freight transport to economic, business, social and technology-related variables. The findings are congruent with the basic need for implementation within the freight industry of updated policy, business transformation, knowledge-intensive services and AI algorithms.

Keywords: Innovation policy and economy; AI; Freight transport industry; Business transformation; Knowledge-intensive companies; KIS (search for similar items in EconPapers)
JEL-codes: F2 M1 M2 O1 O3 Q5 (search for similar items in EconPapers)
Date: 2020
ISBN: 978-1-910129-29-6
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Persistent link: https://EconPapers.repec.org/RePEc:lum:prchap:14-13

DOI: 10.18662/lumproc/ibmage2020/13

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