Search results

This study aims to develop a methodology for predicting the resilience of individual firms to economic crisis, using historical government data to optimize one of the most important and costly interventions that governments undertake, the huge economic stimulus programs that governments implement for mitigating the consequences of economic crises, by making them more focused on the less resilient and more vulnerable firms to the crisis, which have the highest need for government assistance and support.

The authors are leveraging existing firm-level data for economic crisis periods from government agencies having competencies/responsibilities in the domain of economy, such as Ministries of Finance and Statistical Authorities, to construct prediction models of the resilience of individual firms to the economic crisis based on firms’ characteristics (such as human resources, technology, strategies, processes and structure), using artificial intelligence (AI) techniques from the area of machine learning (ML).

The methodology has been applied using data from the Greek Ministry of Finance and Statistical Authority about 363 firms for the Greek economic crisis period 2009–2014 and has provided a satisfactory prediction of a measure of the resilience of individual firms to an economic crisis.

The authors’ study opens up new research directions concerning the exploitation of AI/ML in government for a critical government activity/intervention of high importance that mobilizes/spends huge financial resources. The main limitation is that the abovementioned first application of the proposed methodology has been based on a rather small data set from a single national context (Greece), so it is necessary to proceed to further application of this methodology using larger data sets and different national contexts.

The proposed methodology enables government agencies responsible for the implementation of such economic stimulus programs to proceed to radical transformations of them by predicting the resilience to economic crisis of the firms applying for government assistance and then directing/focusing the scarce available financial resources to/on the ones predicted to be more vulnerable, increasing substantially the effectiveness of these programs and the economic/social value they generate.

To the best of the authors’ knowledge, this study is the first application of AI/ML in government that leverages existing data for economic crisis periods to optimize and increase the effectiveness of the largest and most important and costly economic intervention that governments repeatedly have to make: the economic stimulus programs for mitigating the consequences of economic crises.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.