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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.

This paper aims to investigate the effect of two external factors – the “generalized” competition an organization faces, and the strategy it follows in response to its external environment – on the business value generated by its ICT investment.

For achieving these research objectives econometric models of output are constructed, using firm‐level data from Greek companies, which have been collected through a survey through a structured questionnaire. These econometric models are based on the microeconomic production theory (Cobb Douglas production function). For operationalizing the “generalized competition” an organization faces are used the five dimensions of the generalized competition of M. Porter's “five forces framework”.

Concerning the above generalized competition dimensions it is concluded that higher level of bargaining power of suppliers results in higher ICT business value generation. Also, concerning strategy it is concluded that in organizations following a strategy of frequent introduction of new innovative products and services is generated higher ICT business value.

This paper investigates the effect of external environment related factors on the business value generated by ICT investment. The conclusions constitute of first evidence that there are external conditions that result in higher business value from ICT investment by necessitating a more efficient and effective use of ICT.

The purpose of this study is to develop a novel approach to e-participation, which is based on “passive crowdsourcing” by government agencies, exploiting the extensive political content continuously created in numerous Web 2.0 social media (e.g. political blogs and microblogs, news sharing sites and online forums) by citizens without government stimulation, to understand better their needs, issues, opinions, proposals and arguments concerning a particular domain of government activity or public policy.

This approach is developed and elaborated through cooperation with potential users experienced in the design of public policies from three countries (Austria, Greece and the UK), using a combination of quantitative and qualitative techniques: co-operative development of application scenarios, questionnaire surveys, focus groups and workshops and, finally, in-depth interviews.

A process model for the application of the proposed passive crowdsourcing approach has been developed, which is quite different from the one of the usual active crowdsourcing. Based on it, the functional architecture of the required supporting information and communication technologies (ICT) infrastructure has been formulated, and then its technological architecture has been designed, addressing the conflicting requirements: low response time and, at the same time, provision of sufficiently “fresh” content for policymakers.

Taking into account that traditionally government agencies monitor what the press writes about them, our research provides a basis for extending efficiently these activities in the new electronic media world (e.g. newspapers websites, blogs and microblogs, online forums, etc.) to understand better the needs, issues, opinions, arguments and proposals raised by the society with respect to important domains of government activity and public policies.

The proposed approach provides a new channel for the “voice” of the society to be directly communicated to the government so that the latter can design its policies and activities based on the social needs and realities and not on oversimplified models and stereotypes.

Our paper proposes a novel approach to e-participation, which exploits the Web 2.0 social media – but in a quite different way from previous approaches – for conducting “passive crowdsourcing”, and elaborates it: it develops an application process model for it and also an ICT infrastructure for supporting it, which are quite different from the ones of the existing “active crowdsourcing” approaches.

This paper aims to develop and evaluate, in “real-life” pilot applications, a framework for advanced social media exploitation by government agencies in their policy-making processes to promote public participation and conduct crowdsourcing.

This framework has been developed through cooperation with public sector employees experienced in public policy-making, using both qualitative and quantitative techniques: semi-structured focus group discussions, scenarios development and questionnaire surveys. The evaluation of the framework has been conducted through semi-structured focus group discussions with public sector employees involved in the pilot applications.

A framework has been developed for advanced social media exploitation by government agencies, which is based on the automated posting of policy-related content to multiple social media, and then retrieval and processing of citizens’ interactions with it (e.g. views, likes, comments and retweets), using the application programming interfaces (API) of these social media. Furthermore, a supporting information and communication technologies (ICT) infrastructure and an application process model for it were developed. Its evaluation, based on “real-life” pilot applications, leads to useful insights concerning its capabilities, strengths and weaknesses.

The proposed framework has been evaluated in a small number of pilot applications, so further evaluation of it is required, in various types of government agencies and for different kinds of policy consultations.

The above framework enables government agencies to communicate with wider and more heterogeneous audiences in a short time and at a low cost, increase public participation in their policy-making processes, collect useful knowledge, ideas and opinions from citizens and, finally, design better, more socially rooted, balanced and realistic policies.

This research contributes to the development of knowledge concerning advanced practices for effective social media exploitation in government (which is currently limited, despite the considerable relevant knowledge developed in this area for the private sector), by developing and evaluating a framework for advanced and highly automated exploitation of multiple social media by government agencies. Furthermore, an evaluation methodology for such practices has been developed, which is based on sound theoretical foundations.

The security aspects of public sector information systems are important as the respective systems are often part of critical infrastructures or deal with personal or sensitive data. A set of 53 Greek public sector organizations were investigated by means of a structured questionnaire concerning important aspects of information systems security. We present the relevant theoretical background, the methodology of our research, and an analysis of the obtained results. Greek public sector organizations have only a basic level of information system security awareness. Most care about digital data confidentiality; however, only a small percentage have developed a systematic, complete, and integrated approach towards the security of their information system, including internal audit procedures. The importance of proper training and generally the importance of the human factor for achieving high levels of information systems security is often underestimated.

The purpose of this paper is to investigate the effects of four types of “soft” information and communication technologies (ICT) capital related to ICT knowledge and skills (ICT personnel, ICT training of ICT personnel and users, ICT unit) on the innovation performance of Greek firms. Furthermore, the paper compares these effects with the ones of the hard ICT capital and also of four important “traditional” innovation determinants identified from previous research in this area (demand expectation, price and non-price competition, market concentration).

A quantitative methodology has been adopted for investigating the above effects, based on the estimation of regression models. Using data collected through a survey based on a structured questionnaire from 271 Greek firms, innovation models have been estimated, having as independent variables measures of hard ICT capital, the examined four types of soft ICT capital and also the above traditional innovation determinants.

The paper has been concluded that in the innovation averse Greek national context the examined traditional innovation determinants have very low impact on firms’ innovation performance, however, on the contrary both hard ICT capital, and three out of the four examined types of soft ICT capital (ICT personnel, ICT training of ICT personnel and users) have positive impact on both process and product/services innovation. Furthermore, it has been found that the total effect of these three knowledge and skills related types of soft ICT capital on innovation performance is stronger than the effect of the hard ICT capital.

The main limitations of the paper are that it uses simple innovation performance measures (not distinguishing between different types of innovations), and also is based on firm-level data collected from a single country. The paper has interesting implications for future research on the impact of the relation between ICT and innovation, which should not any more neglect the soft ICT capital, but consider various types of both hard and soft ICT capital.

The results of the paper can be useful to firms’ chief information officers and chief executive officers and also to consultants and practitioners interested in maximizing the exploitation of the innovation potential of ICT, in order to understand the hard and soft aspects of ICT that have to be developed for this purpose and optimize firms’ ICT-related investment.

The limited previous empirical literature concerning the effect of ICT on innovation focus on the hard ICT capital (mainly on ICT equipment) and neglect the role of the soft ICT capital. The paper contributes to fill this research gap, by examining the effects of three types of ICT capital, and also – for comparison and regression models’ completeness purposes – of hard ICT capital and of four traditional innovation determinants, on firms’ innovation performance.

In recent years, there has been much debate about the value generated by the firms’ investments in information technology (IT). Although literature suggests that technology itself will rarely create superiority, web infrastructure can be critical for knowledge sharing and the formation of virtual teams to execute innovation processes which, in turn, may enhance e-innovation and business value. Building on these antecedents, the purpose of this paper is to explore whether and how web infrastructure and e-innovation can create business value by complementing each other.

Based on the resource-based view (RBV) of the firm this paper develops a conceptual model to assess the effects of web infrastructure and e-innovation on business value as well as the complementarity between these resources. To test the associated hypotheses, a regression model was estimated and tested on a large sample of Spanish firms from different industries.

The results show that web infrastructure is not positively related to business value, but on the contrary e-innovation has a positive impact on business value. However, support for complementarity between web infrastructure and e-innovation was not found.

The present study tests the RBV logic, arguing that not all IT resources are source of competitive advantage. In the same vein, this study shows that e-innovation, as it requires combination of IT infrastructure with other unique intangible resources, is much more difficult to imitate, leading to competitive advantages.