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This paper proposes an analysis of occupational accidents in Italy at the regional level. For this purpose, our panel is composed of 20 regions over the 2010–2019 time span.

We apply different econometric estimation techniques (pooled OLS model, panel fixed and random effects models and semiparametric fixed model) using INAIL and ISTAT data. Our models investigate workplace accidents at the regional level by accounting for socioeconomic, labour market and productive system variables and controlling for possible underreporting bias.

Overall results reveal the existence of a relevant under-notification phenomenon of accidents at work with respect to moderate accidents, that is higher especially for the southern regions of Italy. However, when considering as outcome variable an alternative set of more severe workplace accidents our model specification remains highly jointly statistically significant. Among our main findings, the analysis shows that worker skills (blue collar) strongly affect the regional pattern of workplace accidents, i.e. an increase of 1% of low paid employees generates about an increase of 1.8 severe workplace accidents per thousand workers. Moreover, we provide evidence that the size of the firm is inversely related to the occupational accident rates. Finally, our results highlight a nonlinear relationship between GDP and occupational accidents for the Italian regional context, confirmed by the high statistical significance of the quadratic term in all the estimated linear models and by the semi-parametric analysis.

A first element of originality of our study consists of investigating the macro determinants of occupation accidents at a regional Italian level. Second, the empirical literature (Boone and Van Ours, 2006) highlights the possible bias of underreporting behaviours on nonfatal accidents in contrast to fatal accidents that are always reported. From this perspective, we have identified a few analyses (namely, Boone et al., 2011) considering different accident sets characterised by different severity degrees. Thus, this paper contributes to the literature considering five alternative subsets of accidents stratified by degree of severity (i.e. moderate, severe, moderate plus severe, severe plus fatal and total accident rates) to test for possible underreporting bias affecting our econometric model.

The increasing energy consumption in residential buildings poses significant challenges to sustainability, economic efficiency, and environmental health. Despite the availability of numerous energy-saving strategies, their adoption remains inconsistent due to various barriers such as cost, awareness and technical limitations. This paper aims to assess energy-saving strategies mitigating high energy consumption in residential buildings.

The study adopted a quantitative research methodology. A carefully developed study questionnaire was distributed to a group of 20 Delphi experts to collect quantitative data. These experts possess extensive experience in the areas of energy use, energy management and energy savings. The questionnaire was also issued to the end users to gather quantitative data. The collected data were analyzed using descriptive analysis.

The study results indicate a strong preference for certain energy-saving measures, with energy-efficient appliances, occupant behavior adjustments and heating, ventilation and air conditioning (HVAC) system upgrades emerging as the most frequently adopted strategies. Measures such as installing smart thermostats and energy-efficient windows also ranked highly, reflecting their perceived effectiveness in reducing energy consumption. However, strategies like weatherizing buildings and using smart power strips were less frequently implemented, suggesting lower prioritization or barriers to adoption. The findings further emphasize the importance of HVAC-related maintenance and upgrades, highlighted by their top rankings and frequent use, while simpler actions, such as insulation and turning off lights, were ranked lower despite their potential for energy savings.

The study’s limitations include reliance on self-reported data, potential bias in participant responses and a lack of consideration for contextual factors such as regional climate, socioeconomic conditions and technological access that may influence the adoption of energy-saving measures.

This research is distinctive in its integration of the Delphi method with questionnaires to holistically evaluate and assess energy-saving measures for reducing excessive energy use in residential structures. By integrating expert consensus with empirical data from diverse residential contexts, it provides a comprehensive and nuanced understanding of effective energy management practices.

This chapter aims to qualify Local Area as Smart Tourism Local Service Systems (S-TLSS), whose competitiveness and reputation depend on sharing strategies and processes of cohesion based on equifinality among/for stakeholder. The methodology envisages the integration of Service Science Management Engineering and Design (SSME+D) and the Viable Systems Approach (VSA). Thus it describes a S-TLSS in terms of local service system, whose viability requires a ‘smart governmentality’, able to guarantee the management of equity, sustainability and resilience. Referring to human resources coherent with value co-creation processes, S-TLSS implies T-shaped professionals: new kind of individuals who have proficiency in a specific field/discipline (deep professionality) and, at the same time, show capacity to understand and participate in complex projects/systems (broad professionality). Finally, the authors will show a practical application of the system attempting to enhance an Italian territory that is not very attractive to tourists and local residents.

Advances in information technology and the increasing digitalization of the general public have favored the growth of the sharing economy. The sharing economy is based on transactions of idle resources between individuals to satisfy cogent needs. Notwithstanding the great interest in this emerging phenomenon, it is still not clear which factors are driving the shift in consumer consumption behavior from the traditional economy toward this new economic model. Grounded in self-determination theory, we contend that what is needed is a holistic approach that considers the three elements involved in sharing economy transactions, namely (1) consumer motivations, (2) web-based platforms and (3) types of assets exchanged.

To conduct our study, we used the Flash Eurobarometer 467 database titled “The Use of the Collaborative Economy,” collected by the European Union with Flash Eurobarometer datasets and openly available to the public. Consequently, our study aims to provide results based on a large-scale quantitative analysis involving a large number of individuals and multiple sectors.

Our findings provide empirical evidence of the positive effects of the shift in consumption behavior toward the sharing economy brought about by (1) consumers’ intrinsic motivations, (2) the quality of the platform and (3) the human asset-based categories of products offered.

This research seeks to advance understanding of the factors that facilitate the adoption of the sharing economy, and we provide managers and policymakers with suggestions regarding the factors they may leverage to further favor the spread of this economic model.

Recognizing novel entrepreneurial opportunities arising from a crisis is of paramount importance for firms. Hence, understanding the pivotal factors that facilitate firms in this endeavor holds significant value. This study delves into such factors within a representative empirical context impacted by a crisis, drawing insights from existing literature on opportunity recognition during such tumultuous periods.

The authors conducted a qualitative inspection of 14 Italian firms during the COVID-19 pandemic crisis. The authors collected a rich body of multi-source qualitative data, including 34 interviews (with senior managers and entrepreneurs) and secondary data (press releases, videos, web interviews, newspapers, reports and academic articles) in two phases (March–August 2020 and September–December 2020).

The results suggest the existence of a process model of opportunity recognition during crises based on five entrepreneurial influencing factors (entrepreneurial knowledge, entrepreneurial alertness, entrepreneurial proclivity, entrepreneurial personality and entrepreneurial purpose).

Various scholars have highlighted that, in times of crises, it is not easy and indeed very challenging for entrepreneurs to identify novel entrepreneurial opportunities. However, recent research has shown that crises can also positively impact entrepreneurs and their capacity to identify new entrepreneurial opportunities. Given these findings, not much research has analyzed the process by which entrepreneurs identify novel entrepreneurial opportunities during crises. This study shows that some entrepreneurial influencing factors are very important to identify new entrepreneurial opportunities during crises.

Hempseed oil is a valuable emerging food product with recognized health positivity due to its composition. The paper aims to propose a multi-methodological chemical profiling of nine organic hempseed oil samples (different brands and prices) from the retail market, followed by multivariate data analysis.

Fatty acids, tocopherols, squalene, total carotenoids content, phenolic compounds, total phenolic content and anti-radical activity (DPPH• and ABTS•+ assays) were determined through chromatographic and spectrophotometric techniques. Multivariate (explorative and discriminant) analyses were applied to the profiling results to classify samples according to information claimed on the label, i.e. geographical origin (EU vs. non-EU), extraction procedure (cold-pressed vs. not cold-pressed) and price (lower or higher than 10 € per 250 mL).

The chemical analysis confirmed the 3 to 1 ?-6:?-3 ratio and the excellent content in antioxidant species. However, no specific trend of results can be stressed. PCA (after variables selection) highlighted a natural grouping of samples, so three discriminant analyses were performed: kNN, Naïve Bayes and LDA. The best classification efficiency was reached for the extraction procedure verification (93–100% correct classification), followed by geographical origin (83–94%) and prices (81.6–90%).

The integrated approach of chemical profiling coupled with multivariate analyses allowed the assessment of label information of the analyzed organic hempseed oil samples, despite the wide heterogeneity of the selected samples.