The concept of income convergence has drawn the attention of many economists involved in the growth debate (Alataş, 2021). Recent theories of growth and empirical studies suggest that heterogeneity with respect to technological conditions in general and total factor productivity—TFP in particular are identified as the most decisive factors for the rate of income convergence of countries (Islam, 2003). Apparently, depending on whether initial TFP differences decrease or increase over time, income convergence or divergence may be a matter of fact. This has directed researchers’ attention to the concept of technological (TFP) convergence.

Although many empirical studies try to find the answer to the question of technological convergence at the country level (Dowrick & Nguyen, 1989; Wolf, 1991; Dougherty & Jorgenson, 1997; Tebaldi, 2016; Rath & Akram, 2019), regional technological convergence is the research area of relatively modest exploration. However, this situation started to change, since the importance of technological convergence and its determinants have progressively gained attention in both the scientific and the policy domains at the regional level (Rodil-Marzábal & Vence-Deza, 2020). In the context of the Lisbon Agenda (European Council, 2000) and the Europe 2020 strategy (Commission of the European Communities, 2010) goals of making Europe and its regions the most competitive and dynamic knowledge-based economies in the world, it seems crucial to find whether innovation, regarded as the main driver of regional TFP growth (Dettori et al., 2012), can stimulate technological convergence and under what conditions.

Empirical findings suggest that innovation can lead to both technological convergence and divergence processes across regional economies (Walker & Storper, 1989; Verspagen, 2010). In the former, thanks to the diffusion of knowledge and innovation, it is possible for the regions which are technologically lagging behind to catch up with the regions with a higher level of technological advancement. On the other hand, innovations provide additional technological rent and allow leaders to speed up in the technology race. Due to the specificity of knowledge, including its cumulative nature, the relations between the catching-up and speeding-up processes depend on the initial innovative potential of the regions and their absorptive capacities (Dosi, 1988; Verspagen, 2010; Roper & Love, 2006).

From the theoretical perspective, TFP catching-up process can be explained by the concept of ‘advantage of backwardness’ (Vu & Asongu, 2019) and the semi-endogenous R&D-based growth models and their extensions to the regional framework (Jones, 1995; Kortum, 1997; Fukuda, 2017). On the other hand, the conclusion drawn from the first generation of R&D-based endogenous growth models (Segerstrom et al., 1990; Grossman & Helpman, 1991; Baldwin et al., 2001) suggest that a TFP gap between technological leaders and technological followers may widen by R&D investments. Interestingly, the multiple equilibria Schumpeterian R&D models (Howitt & Mayer-Foulkes, 2005) permit the conclusion that different strategies for technology creation and adoption induce convergence clubs formation in TFP. Regional heterogeneity of technology level and the existence of convergence clubs may be also explained by local technological or knowledge spillovers and regional innovation and technological policies, which become more similar over time within certain groups.

The possible multimodality of the distribution of TFP may be also anchored in the theory of innovation geography (Feldman & Kogler, 2010). In the spatial context, the local growth depends on the amount of innovation activity which is carried out locally, and possibly on the ability to take advantage of external technological achievements. At the regional level, technology spillovers have an important spatial component, as it has been argued that spillovers do not travel easily, so that the performance of an individual region is influenced by its geographical location. The existence of localized spillovers of technological knowledge plays a significant role in the regional technological convergence process as the propensity to innovate of each region does depend on that of the surrounding areas. Allowing unequal distribution of TFP, special attention should be paid to the localized spreading of innovation activities.

In the light of the presented considerations, the main aim of this book is to explore the role of innovation in technological convergence in the European regional area. The theoretical framework of the analyses is presented in Chaps. 2 and 3. The former focuses on the spatial aspects of innovation activities, knowledge-based foundation of regional development and policy framework of innovation-driven growth of EU regions. The latter presents the concept of convergence with special reference to technological convergence. Importantly, it gives insights into the role of innovation in technological convergence from the point of view of alternative R&D-based growth theories. In turn, Chap. 4 contains the methodology of research and the results of analyses. We test stochastic convergence, absolute and conditional β-convergence, as well as club convergence. In our analyses we consider technology and innovation spillovers and their impact on the rate of technological convergence. Such approach to testing technological convergence in the European regional area enables us possible to capture a comprehensive picture of the role of innovation activities in shaping TFP trajectories.

We believe that our book will be appealing to researchers interested in regional development, economic and spatial aspects of science and technology progress, and economics of innovation and knowledge. Practitioners and policy-makers may also find it useful as a source of recent results in economic cohesion and technological convergence.