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Purpose: Economic resource constrains in public spending budget in a country, such as Italy, with an ageing population with high incidence of chronic diseases calls for better strategies to improve measuring quality and efficiency in nursing homes (NHs). This paper analyses the efficiency of 40 NHs based in Tuscany considering not only structural characteristics but also quality of care, including residents, relatives and staff satisfaction. Methodology: We run a classic data envelopment analysis (DEA) on data gathered by the NHs’ regional performance evaluation system. We include as inputs the number of total work hours as labour and the daily cost for services as economic resources. As outputs we include measures for quality of care (number of falls, urinary infections and antidepressants), satisfaction (residents, relatives and professionals) and quality of life (days of recreational activities). We run a multivariate regression to analyse the determinants of previously obtained efficiency scores considering factors such as: institutional (ownership), managerial (training) and clinical (patient’s severity). Findings: Results find 35% efficient NHs. Moreover, management and the managerial factor (staff trained in end-of-life support) are predictors of the efficiency score. Originality: Our study uses satisfaction (residents, relatives and professionals) measures as proxy for quality output in the DEA model and measures related to staff management (eg training) as predictors of the efficiency scores.

The paper applies innovation diffusion models to study the adoption process of solar PV energy in the UK from 2010 to 2021 by comparing the trajectories between three main categories, residential, commercial, and utility, in terms of both the number of installations and installed capacity data. The effect of the UK incentives on adoptions by those categories is studied by analyzing the timing, intensity, and persistence of the perturbations on adoption curves. The analysis confirms previous findings on PV adoption, namely the fragile role of the media support to solar PV, the ability of the proposed model to capture both the general trend of adoptions and the effects induced by ad hoc incentives, and the dramatic dependence of solar PV from public incentives. Thanks to the granularity of the data, the results reveal several interesting aspects, related both to differences in adoption patterns depending on the category considered, and to some regularities across categories. A comparison between the models for number of installations and for installed capacity data suggests that the latter (usually more easily available than the former) may be highly informative and, in some cases, may provide a reliable description of true adoption data.

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