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This article identifies and discusses the most common cognitive biases affecting reviewers and editors when they deal with papers, books or any kind of scientific research/project and how they can undermine intellectual capital (IC) in scientific contexts (SCs) as universities and research institutions.

As we posit that certain biases prevent from publishing original and relevant scientific works, literature research and semi-structured interviews with experts have been conducted to identify these biases undermining IC of SCs.

This contribution identifies biases undermining IC in SCs distinguishing the ones influencing editors only (representativeness heuristic, group polarization, country/language and affinity bias) and the ones influencing both editors and reviewers (framing and halo effects, overconfidence/overoptimism, confirmation, adjustment, status quo, bias bias and single-action biases). Also, it provides practical examples on how to overcome them.

This work is based on a limited number of interviews (and most of them to researchers of the economic field). Then, future quantitative researches are needed to increase the generalizability of the data. With regard to implications, the results of this study can be adopted by academies and their components in order to preserve their IC and in particular their knowledge-based resources of human capital.

As this piece of research provides practical examples and it concludes with tips that come from scholars’ experience, it is useful for a wide audience of scholars (in particular for less experienced researchers) pursuing scientific career upgrades and for reviewers looking for useful guidelines.

This study offers a more comprehensive analysis on the factors influencing IC of SCs both mixing literature findings with practical experts’ experience and including different areas of knowledge (e.g. behavioral theories on decision making) as scarcely done in previous studies.

The understudied psychosocial factors affecting Airbnb hosts are examined in this study by focusing on social isolation and willingness to remain as an Airbnb host. The espoused benefits of host flexibility and autonomy have not been fully contextualized in relation to the real demands and costs of hosting.

This study uses Social Support Theory to examine hosts’ perceptions of their positions. Data from 136 Airbnb hosts were analyzed using a structural model to explore relationships between social isolation, work-family conflict, mental wellbeing, and life satisfaction.

The results indicate that higher levels of social isolation were linked to greater work-family conflict, lower mental wellbeing, and reduced life satisfaction. Furthermore, social support was negatively correlated with social isolation.

As a result of social isolation, Airbnb hosts will need to find outside support (e.g. online gig worker communities, mental wellbeing apps) to meet work/life challenges. Gig work platforms should provide tools for gig workers to cultivate social support.

This research presents a needed focus on the paradox of gig work. Airbnb hosting can provide flexible employment and extra income, but it may also lead to social isolation, work-family conflict, and reduced wellbeing. These findings have significant implications for gig workers and contracting organizations, underlining the need to prioritize workers' social connections and overall wellbeing in the increasingly pervasive gig economy.

Advances in information technology and the increasing digitalization of the general public have favored the growth of the sharing economy. The sharing economy is based on transactions of idle resources between individuals to satisfy cogent needs. Notwithstanding the great interest in this emerging phenomenon, it is still not clear which factors are driving the shift in consumer consumption behavior from the traditional economy toward this new economic model. Grounded in self-determination theory, we contend that what is needed is a holistic approach that considers the three elements involved in sharing economy transactions, namely (1) consumer motivations, (2) web-based platforms and (3) types of assets exchanged.

To conduct our study, we used the Flash Eurobarometer 467 database titled “The Use of the Collaborative Economy,” collected by the European Union with Flash Eurobarometer datasets and openly available to the public. Consequently, our study aims to provide results based on a large-scale quantitative analysis involving a large number of individuals and multiple sectors.

Our findings provide empirical evidence of the positive effects of the shift in consumption behavior toward the sharing economy brought about by (1) consumers’ intrinsic motivations, (2) the quality of the platform and (3) the human asset-based categories of products offered.

This research seeks to advance understanding of the factors that facilitate the adoption of the sharing economy, and we provide managers and policymakers with suggestions regarding the factors they may leverage to further favor the spread of this economic model.