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Depression is a common and serious medical illness, considered as a public health issue because it interferes with the interpersonal, social and professional functioning of the individual, and its frequency is constantly increasing. According to a recent review, approximately 34 per cent of nursing students had experienced depression worldwide. The university period may represent a moment in which the mental well-being of students is subjected to stress with a relative predisposition to the development of diseases related to mood disorders. The purpose of this study is to estimate the prevalence and examine the socio-demographic correlates of depressive symptomatology.

In April 2019, a questionnaire was administered to all the nursing students of University of Palermo of the three years of course, accompanied by informed consent. Multivariable logistic regression was performed. The statistical significance level chosen for all analyses was 0.05. The results were analyzed using the STATA statistical software version 14. Results are expressed as adjusted odds ratio (aOR) with 95 per cent confidence intervals.

The sample consists of 493 students who completed the questionnaire, and the average age of the sample participants is 21.88 years. The multivariable logistic regression model shows that the risk to have depressive symptomatology is significantly associated with the following independent variables: female gender (aOR 1.91), being single (aOR 1.87), second year of study (aOR 1.94), third year of study (aOR 1.92), not performing regular physical activity (aOR 1.78) and perceived low health status (aOR 3.08).

This study shows that belonging to the female gender, being further along in the years of study, having a chronic illness and perceiving a low state of health are all factors that can increase the risk of developing the symptoms of depression; rather, regular physical activity, friendship and romantic relationships can be considered factors protecting them from the risk of falling into depression that can undermine both the study and work performance. Certainly, it is important to analyze all the involved variables to improve the global health not only of the nursing students but of all the students.

This study investigates the role of “soft” factors of total quality management – in terms of empowerment and engagement of employees – in facilitating or hindering organizational performance of the university technology transfer offices.

The authors developed an Ordinary Least Squares (OLS), multiple regression model to test if empowerment and engagement affect organizational performance of the university technology transfer offices.

The authors found that “soft” factors of total quality management – in terms of empowerment and engagement – facilitate the improvement of organizational performance in university technology transfer offices.

The authors’ analysis shows that soft total quality management practices create the conditions for improving organizational performance. This study provides practical implications by showing that, in the evaluation of the technology transfer office, not only the “hard” variables (e.g. number of employees and employee experience) but also the “soft” one (e.g. empowerment and engagement) matter. Therefore, university technology transfer managers or university technology transfer delegates should take actions to promote not only empowering employees but also create a climate conducive to employees' engagement in the university technology transfer offices.

With regards to the differences in organizational performances of university technology transfer offices, several studies have focused their attention on technology transfer professionals in technology transfer offices, but only a few of them have examined the “soft side” of total quality management. Thus, this study examines the organizational goals of technology transfer offices through “soft” factors of total quality management in terms of empowerment and engagement employees.

Artificial Intelligence (AI) is a growing technology impacting several business fields. The agricultural sector is facing several challenges, which may be supported by the use of such a new advanced technology. The aim of the paper is to map the state-of-the-art of AI applications in agriculture, their advantages, barriers, implications and the ability to lead to new business models, depicting a future research agenda.

A structured literature review has been conducted, and 37 contributions have been analyzed and coded using a detailed research framework.

Findings underline the multiple uses and advantages of AI in agriculture and the potential impacts for farmers and entrepreneurs, even from a sustainability perspective. Several applications and algorithms are being developed and tested, but many barriers arise, starting from the lack of understanding by farmers and the need for global investments. A collaboration between scholars and practitioners is advocated to share best practices and lead to practical solutions and policies. The promising topic of new business models is still under-investigated and deserves more attention from scholars and practitioners.

The paper reports the state-of-the-art of AI in agriculture and its impact on the development of new business models. Several new research avenues have been identified.

The purpose of this paper is to analyse the role that different financial sources and financial specialization have on private research and development (R&D) activity in OECD countries.

The authors developed several panel regressions choosing as a final model a two-way random effects regression to understand which funding sources are related to the R&D expenditure, and how financial specialization has links to the private portion of R&D aggregated expenditure. The authors include data from the years 2000 to 2016 for OECD countries.

The results reinforce the critical role that stock markets have in enhancing private R&D and that bond markets have an inverse relationship with private R&D national expenditures. The authors do not find evidence of a link between bank sources and private R&D. Specialized financial systems (banking or market) support innovation in a better way than a mixed arrangement of those two systems.

The findings of this study have considerable policy implications. Policymakers need to be aware of these results, given that some variables related to financial markets, seems to boost the inputs for R&D. In the long term, this could be a signal that national and regional systems of innovation need a broad view of the factors hampering scientific activity, and also a signal that there are other ways to impact the results of the complex innovation activity through the development of stronger financial systems backing up national systems of innovation.

The authors found that the long discussion about the financial system that a country has to choose to enhance growth with R&D&I may have been misleading the public policy. The findings show that rather than a bank or a stock market financial system, economies looking to boost R&D&I, must specialize in one of the two systems, deepen these and generate the appropriate policies to promote science, technology and innovation using those financial markets.