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This paper aims to investigate the role of predictive models in the learning and decision-making processes of strategic management. The rapid advancement of digitalisation has contributed to increasing the complexity of the worldwide economy and led to various new competitive dynamics.

To achieve this purpose, a literature review has been carried out and a predictive model based on Watson, an IBM supercomputer, is presented as a qualitative process model.

Specific insights derived from a review of the literature highlight organisations' need to modify their decision- and strategy-making processes, which are increasing in speed and frequency, thus also leading to the formulation of emergent and trigger event strategies based on the identification of conditions that require the revision of all or part of the firm's strategy. Predictive models, acting as filters, transform data into informative knowledge that decision-makers can interpret based on individual domain knowledge.

From a theoretical point of view, this paper contributes to the field of digital transformation by proposing the economics of complexity as a paradigm through which to observe and study the issue of predictive models in strategic management. Additionally, the authors analyse the phenomenon from a cognitive perspective, defining the new learning dynamics of digital transformation and the social learning cycle triggered by big data and predictive models. From a managerial and policy-making point of view, this suggests the need to re-shape traditional education contents and dynamics and foster skills that are multi-disciplinary, multi-domain, multi-empathic, multi-interaction and multi-communication between people and things.

This paper aims to provide a contribution on the diffusion of Industry 4 (I4.0)-related knowledge in industrial districts (IDs). The main goal is to examine the dissemination of I4.0 knowledge, exploring the main mechanisms for its spreading and highlighting the main factors shaping such processes. Focus is on dissemination processes in IDs active in traditional industries, which could represent the “periphery” of I4.0 application context.

The methodology is qualitative. Notably, this paper presents a case study of the Pesaro ID specialized in furniture/woodworking machinery sector. A total of 18 in-depth one-to-one interviews have been conducted with relevant informants from a variety of organizations within the cluster: companies, institutions and universities.

The complexity of I4.0 requires a combination of traditional mechanisms with innovative ones within IDs characterized by the emergence of new players, activities and resources. These changes led to three main evolving patterns: the horizon of I4.0 upgrading shows blurred boundaries in terms of sectors and geographic location, the I4.0 diffusion appears fragmented in terms of initiatives and projects by both firms and institutions and the dissemination of I4.0 knowledge pushes ID firms and institutions to pursue deliberate initiatives leading to innovative forms of “collective” cooperation.

This paper contributes to both theory and practice. From the theoretical point of view, this paper contributes to the literature on innovation in IDs and clusters on two interrelated grounds. First, it provides further research on I4.0 and IDs and clusters. Second, it contributes to the stream of research on knowledge creation and diffusion in IDs and clusters, providing empirically based insights over emerging local learning processes in IDs. Moreover, relevant managerial and policy implications stem from the analysis.

This paper aims to provide a snapshot of various countries’ contributions to value produced along global value chains (GVCs). It focusses on manufacturing activities and their evolution over time, in the context of GVC regionalisation.

The Trade in Value Added (TiVA) and World Integrated Trade Solution databases for the period of 2005-2015 were used to explore the case of Italy and its industries’ specialisations (Made in Italy): fashion, furniture, automotive and machinery traditionally organised into clusters. Various analyses were used to show the dynamics of gross import–export and imported–exported value-added. Moreover, the revealed comparative advantage index was computed to test whether the Made in Italy sector remains a source of competitive advantage for Italy within GVCs.

The results highlight how the geography of value-added is changing over time, with growing importance placed on the countries close to Italy and with a different pace according to each considered GVC.

The paper applied new methods to compare trade and analyse value-added dynamics through a recent database released by the Organization for Economic Co-operation and Development within the TiVA initiative that is useful for scholars and policymakers.