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This study aims to ascertain the intentions of risk managers to use artificial intelligence in performing their tasks by examining the factors affecting their motivation.

The study employs an integrated theoretical framework that merges the third version of the technology acceptance model 3 (TAM3) and the unified theory of acceptance and use of technology (UTAUT) based on the application of the structural equation model with partial least squares structural equation modeling (PLS-SEM) estimation on data gathered through a Likert-based questionnaire disseminated among Italian risk managers. The survey reached 782 people working as risk professionals, but only 208 provided full responses. The final response rate was 26.59%.

The findings show that social influence, perception of external control and risk perception are the main predictors of risk professionals' intention to use artificial intelligence. Moreover, performance expectancy (PE) and effort expectancy (EE) of risk professionals in relation to technology implementation and use also appear to be reasonably reliable predictors.

Thus, the study offers a precious contribution to the debate on the impact of automation and disruptive technologies in the risk management domain. It complements extant studies by tapping into cultural issues surrounding risk management and focuses on the mostly overlooked dimension of individuals.

Yet, thanks to its quite novel theoretical approach; it also extends the field of studies on artificial intelligence acceptance by offering fresh insights into the perceptions of risk professionals and valuable practical and policymaking implications.

This paper aims to investigate the factors influencing chief executive officers’ (CEOs') intentions to implement cloud technology in Italian small and medium-sized enterprises (SMEs).

The study proposes a model that integrates the theoretical construct of the technology acceptance model (TAM) with a classification of perceived benefits and risks related to cloud computing. The study employs a structural equation modeling approach to analyze data gathered through a Likert scale-based survey.

The findings indicate that risk perception has a strong negative effect on the intention to introduce cloud technology in firms. This effect is partially offset by the perceived ease of use of the technology.

The study provides a new theoretical framework that integrates the TAM and a classification of perceived risks to provide a clear view of management's cognitive processes during technological change. Moreover, the results show the main factors influencing decisions regarding the implementation of cloud computing in firms in light of the perception of risks. Finally, this study provides interesting findings for cloud service providers (CSPs) about their customers' decision-making processes.

This study aims to investigate whether the characteristics of the chief financial officer (CFO) have an impact on the intensity of the corporate research and development (R&D) investment.

Based on hand-collected data for the CFOs of a sample of the largest European listed companies for the period 2013–2016, this study uses regression analyses to test empirically the association of CFO education, CFO gender and CFO age with R&D investment intensity.

The presence of female CFOs, CFOs with a Master of Business Administration (MBA) or Doctor of Philosophy (PhD) degree and older CFOs is positively associated with the intensity of R&D investment.

This study relies on some observable characteristics of CFOs and focuses on large listed companies.

The results of this study may help investors, stakeholders and practitioners to understand better which type of CFO characteristics are more likely to result in higher firm-level R&D investment intensity.

This study offers the first insights into the impact of CFOs, as the most prominent C-suite executives, on the level of corporate investments in R&D activity.