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This paper aims to identify the specific contextual constraints that women might face in becoming entrepreneurs; to correlate these constraints with the motivations that have determined this choice; and to ascertain how strongly constraints and motivations are correlated with individual rewards in terms of personal satisfaction and economic payoffs.

The empirical base is a survey that the authors conducted among female entrepreneurs in a Southern province of Italy in 2012. Data are analyzed through a correspondence and cluster analysis. The socio-economic context of the province within which these female-led firms operate is taken into account by means of a correspondence canonical analysis.

In terms of results: first, two-thirds of female entrepreneurs in the province are positively motivated, and this is a determining factor in their choice to become entrepreneurs. This translates into they also being satisfied with the choice they made. Second, contrary to the expectations, being positively motivated and satisfied holds both for firms operating in more dynamic and demanding sectors and for small firms using little financial or human capital.

The chosen research approach has allowed to identify the most important decisional variables that affect female entrepreneurial choice. However, as most of the variables are categorical, the research’s results remain descriptive.

Positive motivations and personal rewards are clearly relevant for women making an entrepreneurial choice. However, they are not enough to stimulate fully the potential for growth of their enterprises: education and a social environment conducive to female creative expression are also necessary. To this end, the authors suggest that an important function of change could be played in particular by universities by fostering a culture of creativity and entrepreneurship.

By stressing the connections between positive motivations and wellbeing, the authors suggest that the promotion of women’s entrepreneurial choices through networks and education generates more than purely economic benefits. It also has positive effects on their quality of life and on social welfare as well.

This paper responds to a need – not yet fulfilled in the literature – to better understand the relations between women’s motivation, satisfaction and the type of business selected.

Most of the 3D printing machines do not comply with the requirements of on-site, large-scale multi-story building construction. This paper aims to propose the conceptualization of a tower crane (TC)-based 3D printing controlled by artificial intelligence (AI) as the first step towards a large 3D printing development for multi-story buildings. It also aims to overcome the most important limitation of additive manufacturing in the construction industry (the build volume) by exploiting the most important machine used in the field: TCs. It assesses the technology feasibility by investigating the accuracy reached in the printing process.

The research is composed of three main steps: firstly, the TC-based 3D printing concept is defined by proposing an aero-pendulum extruder stabilized by propellers to control the trajectory during the extrusion process; secondly, an AI-based system is defined to control both the crane and the extruder toolpath by exploiting deep reinforcement learning (DRL) control approach; thirdly the proposed framework is validated by simulating the dynamical system and analysing its performance.

The TC-based 3D printer can be effectively used for additive manufacturing in the construction industry. Both the TC and its extruder can be properly controlled by an AI-based control system. The paper shows the effectiveness of the aero-pendulum extruder controlled by AI demonstrated by simulations and validation. The AI-based control system allows for reaching an acceptable tolerance with respect to the ideal trajectory compared with the system tolerance without stabilization.

In related literature, scientific investigations concerning the use of crane systems for 3D printing and AI-based systems for control are completely missing. To the best of the authors’ knowledge, the proposed research demonstrates for the first time the effectiveness of this technology conceptualized and controlled with an intelligent DRL agent.

The results provide the first step towards the development of a new additive manufacturing system for multi-storey constructions exploiting the TC-based 3D printing. The demonstration of the conceptualization feasibility and the control system opens up new possibilities to activate experimental research for companies and research centres.