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This study aims to offer an empirical analysis to identify the relation between the adoption of knowledge management practices and the improvement of social innovation capabilities as an outcome of the knowledge sharing. Specifically, social innovation capabilities are triggered by knowledge-sharing enablers, such as intrinsic and extrinsic socially driven motivations.

Based on a sample from 300 nonprofit organizations, the study explains causal relationships in terms of the multiplicity of triggers that act on a social innovation capability. The research applied the partial least squares structural equation modeling (PLS-SEM) method through SmartPLS 3.3 software. Data are collected from an online survey and highlight the employees’ and volunteers’ boost in nonprofit organizations to seek positive social change as a priority goal of their business model.

The results support the existence of a direct and positive relationship between knowledge-sharing enablers (i.e. intrinsic and extrinsic socially driven motivations), the mechanism of the formation of knowledge-sharing behaviors (an inside-out and outside-in process) and social innovation capabilities.

The study combines the open innovation framework with social innovation activities and investigates the role of knowledge sharing in the building of social innovation capabilities. To the best of the authors’ knowledge, the paper is the first attempt to describe a synergic framework, including open innovation literature, social innovation capability and knowledge-sharing processes.

This paper is a part of the research stream that focuses on the processes of distribution of knowledge flows along the boundaries of the organization. Thus, this study broadens the field of knowledge management and social innovation initiatives.

The purpose of this paper is to provide a dating system for the Italian residential real estate market from 1927 to 2019 and investigate its interaction with credit and business cycles.

To detect the local turning point of the Italian residential real estate market, the authors apply the honeycomb cycle developed by Janssen et al. (1994) based on the joint analysis of house prices and the number of transactions. To this end, the authors use a unique historical reconstruction of house price levels by Baffigi and Piselli (2019) in addition to data on transactions.

This study confirms the validity of the honeycomb model for the last four decades of the Italian housing market. In addition, the results show that the severe downsizing of the housing market is largely associated with business and credit contraction, certainly contributing to exacerbating the severity of the recession. Finally, preliminary evidence suggests that whenever a price bubble occurs, it is coincident with the start of phase 2 of the honeycomb cycle.

To the best of the authors’ knowledge, this is the first time that the honeycomb approach has been tested over such a long historical period and compared to the cyclic features of financial and real aggregates. In addition, even if the honeycomb cycle is not a model for detecting booms and busts in the housing market, the preliminary evidence might suggest a role for volume/transactions in detecting housing market bubbles.