Factor and cluster analysis are used to identify different methods that public sector agencies in Europeuse to innovate, based on data from a 2010 survey of 3273 agencies. The analyses identify three types ofinnovative agencies: bottom-up, knowledge-scanning, and policy-dependent. The distribution of bottom-up agencies across European countries is positively correlated with average per capita incomes while thedistribution of knowledge-scanning agencies is negatively correlated with income. In contrast, there isno consistent pattern by country in the distribution of policy-dependent agencies. Regression resultsthat control for agency characteristics find that innovation methods are significantly correlated with thebeneficial outcomes of innovation, with bottom-up and knowledge-scanning agencies out-performingpolicy-dependent agencies

Destination familiarity is thought to critically influence tourists’ decision-making processes. Yet the role of familiarity in shaping tourists’ and residents’ image of, and loyalty to, a destination remains uncertain. This research tests a complex and holistic model of familiarity, affective, cognitive and overall images, and the conative behavioural intentions of visiting and recommending the destination for both residents and visitors in the context of the emerging tourism destination of Molise, Italy. The results reveal that residents and visitors differ in terms of their familiarity and intention to visit a place, with familiarity being less likely to influence residents’ intentions. There is heterogeneity between residents and visitors’ affective image and intention to visit, as well as between their overall image and intention to recommend Molise. Hence, unlike visitors, residents are more likely to respond to factual cognitive imaging, rather than emotional messaging, suggesting that shifting residents’ perceptions of place image requires a different approach to that of visitors. Future research should seek to confirm the relationships in a multi-destination study.