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Purpose Public sector has started exploiting artificial intelligence (AI) techniques, however, mainly for operational but much less for tactical or level tasks. The purpose of this study is to exploit AI for the highest strategic-level task of government: to develop an AI-based public sector data analytics methodology for supporting policymaking for one of the most serious and large-scale challenges that governments repeatedly face, the economic crises that lead to economic recessions (though the proposed methodology is of much more general applicability). Design/methodology/approach A public sector data analytics methodology has been developed, which enables the exploitation of existing public and private sector data, through advanced processing of them using a big data-oriented AI technique, “all-relevant” feature selection, to identify characteristics of firms as well as their external environment that affect (positively or negatively) their resilience to economic crisis. Findings A first application of the proposed public sector data analytics methodology has been conducted, using Greek firms’ data concerning the economic crisis period 2009–2014, which has led to interesting conclusions and insights, revealing factors affecting the extent of sales revenue decrease in Greek firms during the above crisis period and providing a first validation of the methodology used in this study. Research limitations/implications This paper contributes to the advancement of two emerging highly important, for the society, but minimally researched, digital government research domains: public sector data analytics (and especially policy analytics) and government exploitation of AI. It exploits an AI feature selection algorithm, the Boruta “all-relevant” variables identification algorithm, which has been minimally exploited in the past for public sector data analytics, to support the design of public policies for addressing one of the most serious and large-scale economic challenges that governments repeatedly face: the economic crises. Practical implications The proposed methodology allows the identification of characteristics of firms as well as their external environment that affect positively or negatively their resilience to economic crisis. This enables a better understanding of the kinds of firms that are more strongly hit by the crisis, which is quite useful for the design of public policies for supporting them; and at the same time reveals firms’ practices, resources, capabilities, etc. that enhance their ability to cope with economic crisis, to design policies for promoting them through educational and support activities. Social implications This methodology can be very useful for the design of more effective public policies for reducing the negative impacts of economic crises on firms, and therefore mitigating their negative consequences for the society, such as unemployment, poverty and social exclusion. Originality/value This study develops a novel approach to the exploitation of public and private sector data, based on a minimally exploited, for such purposes, AI technique (“all-relevant” feature selection), to support the design of public policies for addressing one of the most threatening disruptions that modern economies and societies repeatedly face, the economic crises.

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Purpose Previous empirical research on cloud computing (CC) adoption factors has examined the effects of only a small number of firm’s characteristics on CC adoption. The purpose of this paper is to investigate empirically the effects of a wide set of firm’s characteristics, which concern four important aspects of it, its strategy, processes, personnel and technology, on the propensity to adopt CC. Design/methodology/approach Having as theoretical background the technology, organization and environment (TOE) theory of technological innovation adoption, in combination with Scott-Morton’s framework on firm’s main elements, ten research hypotheses have been developed based on previous CC and management literature. They were tested using data collected through the e-Business W@tch Survey of the European Commission from 676 European firms from three traditional manufacturing sectors. Findings The results reveal three characteristics of a firm that affect positively its propensity to adopt CC for all firm sizes: the adoption of ICT investment reduction strategy, the adoption of product/service innovation strategy and the sophistication of firm’s administration support ICT infrastructure. Furthermore, they reveal four additional characteristics of a firm that affect positively the propensity for CC adoption only in the small firms: the adoption of process innovation strategy, the employment of ICT personnel, as well as the sophistication of firm’s production support and e-sales ICT infrastructures. Research limitations/implications First, this study provides a theoretical foundation for the elaboration of the organizational perspective of the TOE theory of technological innovation adoption, which opens a new stream of CC adoption factors research, investigating the effects of a wide range of firm’s characteristics on CC adoption. Second, based on the above foundation, this study enriches substantially the empirical literature on CC adoption factors. The main limitation of this study is that it has been based on data from only three European manufacturing sectors. Practical implications The findings provide new interesting insights concerning specific firm’s characteristics and therefore internal conditions that increase its propensity for CC adoption, and reveal specific kinds of strategy and ICT infrastructures for which CC is more appropriate and beneficial. Originality/value The authors have developed a theoretical foundation for extending our knowledge concerning the characteristics and internal conditions of firms that favor/promote the adoption of CC, which supports and enables the substantial extension of the existing knowledge base on CC adoption factors. Based on this theoretical foundation, the authors have formulated and tested ten research hypotheses concerning effects of firm’s strategic directions, processes, ICT infrastructures and ICT personnel, which have not been investigated previously, on CC adoption propensity.