Suchergebnisse

Despite growing recognition of the critical role of parents in children's early development, parenting education programs and interventions typically have had limited impacts on children's outcomes. To design programs and policies that are more responsive to families' needs and constraints, policymakers need a better understanding of the lived experiences of families. In this article, we argue that qualitative video-ethnographic approaches offer an innovative and useful supplement to policy researchers' usual tool kit. Taking a holistic approach to parent–child interactions and filming families in their natural environments over an extended period provides policy researchers with new data to inform future parenting initiatives. To assist researchers interested in undertaking a video-ethnographic study, we discuss our experiences with the New Jersey Families Study, a 2-week, in-home video study of 21 families with a 2- to 4-year-old child. This is the first time anyone has attempted an in-home naturalistic observation of this breadth, intensity, or duration. We highlight the potential of this method for policy relevance along with its associated challenges.

Theory production has been a central focus of management research for decades, mostly because theory legitimizes both management research and, through its application, management practice as professional endeavors. However, such an emphasis on theory glosses over one of its constraining and particularized roles in scientific explanation, namely that theory codifies predictive knowledge. Committing to a 'traditional' or 'critical' understanding of theory thus amounts to embracing the view that prediction is achievable within a circumscribed field of study. Such an embrace is non-controversial in natural science. However, within the realm of management studies, it necessitates and smuggles in a strawman view of human existence, one which does not accommodate freedom and responsibility. This limitation of management theory explains its inadequate utility. This article argues that alternative avenues for management research exist. JEL CLASSIFICATION: M10

Despite the large number of technology-intensive organisations, their corporate know-how and underlying workforce skill are not mature enough for a successful rollout of Artificial Intelligence (AI) services in the near-term. However, things have started to change, owing to the increased adoption of data democratisation processes, and the capability offered by emerging technologies for data sharing while respecting privacy, protection, and security, as well as appropriate learning-based modelling capabilities for non-expert end-users. This is particularly evident in the energy sector. In this context, the aim of this paper is to analyse AI and data democratisation, in order to explore the strengths and challenges in terms of data access problems and data sharing, algorithmic bias, AI transparency, privacy and other regulatory constraints for AI-based decisions, as well as novel applications in different domains, giving particular emphasis on the energy sector. A data democratisation framework for intelligent energy management is presented. In doing so, it highlights the need for the democratisation of data and analytics in the energy sector, toward making data available for the right people at the right time, allowing them to make the right decisions, and eventually facilitating the adoption of decentralised, decarbonised, and democratised energy business models.

Introduction -- Extending goal ambiguity research in government: from organizational goal ambiguity to program goal ambiguity -- Budgets and financial management -- Organizational structure and public service performance -- Red tape: the bane of public organizations? -- Managerial networking, managing the environment, and program performance: a summary of findings and an agenda -- Public service motivation and performance -- Organizational diversity and public service performance -- Performance management: does it work? -- Strategy: which strategic stances matter? -- Methods -- Conclusion: enriching the field.

This deliverable describes work done implementing in four different Data Management Plan tools support for machine-actionable data management plans (maDMP) that is necessary for actionable modular data management plans for brokering distributed resources. The work covers the applications of the maDMP and lessons learned. ; The EOSC-Nordic project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 857652. This deliverable is EC submitted, not approved.