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Highly deprived urban contexts, such as informal settlements in the global south, can turn into niches of extreme innovation and sparkle ingenuity out of necessity. But what are the rationales behind the participation of disadvantaged communities in social innovations? Why do they engage in grassroots innovations? What is it that makes these grassroots try novelties and continue experimenting with them, even when the perceived benefits are not clear yet? This paper aims to examine and conceptualize the rationales for engaging in grassroots financial innovations in the context of extremely deprived urban settings.

This paper is based on the case of grassroots organizations which have started experimenting with the development of a community currency in Kisumu, Kenya. This paper is informed by in-depth interviews with members of three grassroots organizations involved in the community currency, together with observations and meeting participation since 2019.

The rationales argued by the participants for engaging in this grassroots innovation are framed in various ways: as a means for seeking poverty alleviation (the development framing); as a challenge to conventional imaginaries of innovations (the digital framing); and as an innovation embedded in community and trust relations (the community framing). These framings have a mobilizing effect that initially draws participants into the innovation. Yet, what explains persistent participation despite the decreasing influence of these framings over time is the organizational space and strategies of incompleteness accommodating these experiments.

This paper contributes to the emerging body of grassroots innovations movements literature. While research has progressed in its understandings of the challenges of scaling up innovative practices, the examination of the grassroots initiatives stemming from extremely deprived settings, and the rationales and framings behind, have been under examined. This paper comes to bridge this gap.

The purpose of this paper is to analyze the evidence-based research strategy (EBRS) used to evaluate eight projects that applied the neighborhood approach for disaster risk reduction (NA-DRR) in informal urban settlements in Colombia, Guatemala, Haiti, Honduras, Jamaica and Peru, between 2012 and 2017.

The study covers the first five of the seven EBRS stages: first, identify relevant interventions; second, prepare evaluation questions; third, select evidence sources and implement a search strategy; fourth, appraise evidences and identify gaps; fifth, create an evaluation design to include an extensive literature review, followed by a mixed research method with surveys, focus groups and interviews; disaster risk modeling; georeferencing analysis; and engineering inspections. The last two stages: sixth, apply the evidence, and seventh, evaluate the evidence application, will be addressed in a near future.

Even though the reference to “evidence” is frequent in the DRR field, it is largely based on descriptive processes, anecdotal references, best practices, lessons learned and case studies, and particularly deficient on the subject of informal and precariousness settlements. The evaluation allowed a deep and broad analysis of NA-DRR in urban informal settlements, comparing it with other DRR strategies implemented by different stakeholders in fragile urban settings, assessing the effectiveness and sustainability of the various DRR interventions.

The abundant data, information and knowledge generated will serve as foundation for forthcoming thematic peer-reviewed publications informing evidence-based DRR research, policy and practice, with emphasis on informal and precariousness settlements in particular.

Recommendation systems are a fundamental tool for hotels to adopt a differentiating competitive strategy. The main purpose of this work is to use machine learning techniques to treat imbalanced data sets, not applied until now in the tourism field. These techniques have allowed the authors to analyse the influence of imbalance data on hotel recommendation models and how this phenomenon affects client dissatisfaction.

An opinion survey was conducted among hotel customers of different categories in 120 different countries. A total of 135.102 surveys were collected over eleven quarters. A longitudinal design was conducted during this period. A binary logistic model was applied using the function generalized lineal model (GLM).

Through the analysis of a representative amount of data, the authors empirically demonstrate that the imbalance phenomenon is systematically present in hotel recommendation surveys. In addition, the authors show that the imbalance exists independently of the period in which the survey is done, which means that it is intrinsic to recommendation surveys on this topic. The authors demonstrate the improvement of recommendation systems highlighting the presence of imbalance data and consequences for marketing strategies.

The main contribution of the current work is to apply to the tourism sector the framework for imbalanced data, typically used in the machine learning, improving predictive models.