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Due to population ageing, the European Union (EU) has adopted active ageing as a guiding principle in labour and retirement policies. Among the strategies for active ageing, age-friendly workplaces play a crucial role. This study compares age-friendly human resource (HR) practices in the Baltic and Nordic countries. The latter are pioneers in active ageing, and as the employment rate of older employees in the Baltics is like that in the Nordic countries, we may assume equally age-friendly workplaces in both regions.

We used the latest CRANET survey data (2021–2022) from 1,452 large firms in seven countries and constructed the fuzzy logic model on age-friendliness at the workplace.

Despite a high employment rate of older individuals in the Baltics, HR practices in these countries fall short of being age-friendly compared to their Nordic counterparts. Larger firms in the Nordic countries excel in every studied aspect, but deficiencies in the Baltics are primarily attributed to the absence of employer-provided health and pension schemes. The usage of early retirement is more frequent in the Nordic countries; however, its conceptualisation as an age-friendly HR practice deserves closer examination. Our findings suggest that the success of active ageing in employment has translated into age-friendly HR practices in larger organisations in the Nordics, but not in the Baltics. It is likely that high employment of older individuals in Estonia, Latvia and Lithuania is a result of the relative income poverty rate.

Our model represents one of the few attempts to utilise fuzzy logic methodology for studying human resource practices and their quantitative evaluation, especially concerning age-friendly workplaces.

This study aims to predict the dark side of knowledge management risk to innovation in Portuguese small and medium enterprises (SMEs). It examines the spinner innovation model factors of knowledge creation, knowledge transfer, private knowledge, public knowledge and innovation in uncertain environments.

The authors developed a conceptual model to support the analysis. The survey data stemmed from a sample of 208 Portuguese SMEs in Portugal. The authors analyzed the primary data from the ad hoc survey using the data mining (deep learning) technique.

The research sets out and tests factors relevant to understanding how to predict innovation in uncertain business environments. This study identifies four factors fostering innovation in SMEs: knowledge creation, knowledge transfer, public knowledge management and private knowledge management. Knowledge creation showed the best return and presented the closest relationship with innovation.

Innovation models generally measure the relationships between variables and their impacts on the economy (economic and regional development). Predictive models are considered in the literature as a gap to be filled, especially in an uncertain environment in the SME context.