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Using the social constructivist perspective of learning, this study aims to examine the patterns and the key areas of entrepreneurial learning based on a case study of 16 participants who were the incubatees of two technology-based business incubators in China. The key research question is: how do novice entrepreneurs, focusing on technology-based business incubators, learn from a social constructivist perspective?

The researchers applied a qualitative methodology in this study as they wanted to understand better the complexity of the learning process that is hard to achieve quantitatively. The qualitative data was collected through in-depth interviews with the incubatees, who were the managers and owners of their businesses. The interviews with the entrepreneurs were mainly focused on the learning patterns and the factors influencing learning through the use of the critical incident technique.

This will allow incubator managers to better evaluate the extent of effective entrepreneurial learning within the incubator's eco-system. The results show that the participants learn through socially constructivist systems that are structured around the support provided by the incubators. Learning in this context takes place in an extended spectrum, and participants are more interested in learning from networking with experienced entrepreneurs rather than from other incubatees or formal courses. Findings of this study help incubator managers and novice entrepreneurs to better shape learning and teamwork in an effort to improve the learning process. Policy makers should consider introducing schemes that encourage novice entrepreneurs to exhibit the creativity and innovation behaviour reported by experienced entrepreneurs.

The focus of this study is primarily on incubators as the context of learning, whereas the macro-environmental factors, such as the socio-cultural and regulatory environments in China, were considered as playing a subtle role and would affect the incubatees' learning indirectly. The paper is based on a relatively small sample size and is geographically located in Ningbo, China. As such, the authors call for further research for comparative studies with a larger sample size so that a possible theory of entrepreneurial learning in the context of incubators might emerge in the future.

We identify lessons from a project sponsored by a large charitable trust, which sought to build capability for end-of-life (EOL) care in Hong Kong through interdisciplinary and multi-agency collaboration.

An in-depth case study drawing on 21 in-depth interviews with diverse stakeholders was conducted. Lyman et al.’s (2018) model of organisational learning (OL) in healthcare settings was applied to analyse the relative emphasis on particular contextual factors and mechanisms, and to identify outcomes perceived to have been achieved.

Infrastructure such as materials for assessment and education received the most emphasis among the contextual factors and deliberate learning such as training sessions received the greatest attention among the mechanisms. While perceptions indicated that desired outcomes were being achieved in terms of social impact, there were relatively few mentions of “soft” factors such as enhanced motivation, leadership or OL skills among staff.

This study extends the literature on how to create valuable social impact through OL. While prior studies have examined social impact in terms of solutions for social and environmental problems, ours is one of the few that examines how improvements are made to organisations’ capability to deliver such impacts in the context of healthcare.

In this study, the deployment of an integrated Quality Function Deployment (QFD) decision framework is presented to help cities design targeted strategies to become “smart”. Arguably smart cities leverage advanced technologies to enhance their smartness to improve everyday urban life. To this end, a QFD – Analytic Hierarchy Process – Analytic Network Process (QFD-AHP-ANP) framework is proposed to deliver guidance for selecting the appropriate mix of smart technologies based on the specific smart needs of each city.

The AHP and ANP methods are incorporated into QFD to enhance its methodological robustness in formulating the decision problem. AHP accurately captures and translates the “Voice of the Experts” into prioritized “Smart City” dimensions, while establishing inter-relationships between these dimensions and “Smart City Technologies”. Meanwhile, ANP explores tradeoffs among the technologies, enabling well-informed decisions. The framework’s effectiveness is evaluated through an illustrative application in the city of Thessaloniki.

Applying the framework to this real-world context confirms its practicality and utility, demonstrating its ability to particularize local, social, political, environmental and economic trends through the resulting mix of technologies in smart urban development strategies.

The importance of this study lies in several aspects. Firstly, it introduces a novel QFD decision framework tailored for smart city strategic planning. Secondly, it contributes to the operationalization of the smart city concept by providing guidance for cities to effectively adopt smart technologies. Finally, this study represents a new field of application for QFD, expanding its scope beyond its traditional domains.