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This study aims to investigate the effect of risk tolerance on the individual choice of adopting Bitcoin in the form of making and receiving payment and receiving compensation.

The study uses data collected from an anonymous survey of 225 undergraduate and graduate students to measure their risk attitude using the general risk-taking propensity scale proposed by Zhang et al. (2018) and the risk-taking index, proposed by Nicholson et al. (2018). After controlling for a variety of personal traits, the study uses logistic regression to identify the predicted probabilities and marginal effects on individual choice of adopting Bitcoin.

The findings of this study suggest that individuals with a higher risk-seeking attitude are more likely to choose to receive payment for goods they sell in Bitcoin and more likely to choose to receive a portion of their compensation in cryptocurrency. Individuals in the higher-income groups are more likely to adopt Bitcoin 46% and 65% than their lower 14% and 45% and middle income 4% and 18% counterparts. While there was no statistically significant difference between males and females in adopting Bitcoin, respondents between the age of 26 and 29 were more likely to adopt Bitcoin. The effect on receiving gold was slightly smaller but highly comparable to that of receiving Bitcoin, which highlights a similar perception of risk toward the Bitcoin and gold.

The study uses a new data set collected by surveying 225 individuals and two different risk measurements to identify the relationship between perceived risk and Bitcoin adoption.

This study aims to identify the effect of selected agro-industrial factors associated with the olive oil phenolic composition, total phenolic content (TPC), antioxidant capacity and oxidative stability index (OSI). The study also aims to assess the relationship between the quality indices and each of the individual phenol, TPC, antioxidant capacity and OSI.

Olive oil samples (n=108) were collected from Lebanese northern (Akkar and Zgharta-Koura) and southern (Hasbaya and Jezzine) regions, at three harvesting times (early, intermediate, late) and using different types of mills (traditional, sinolea, two- and three-phase decanters). The samples were analyzed using official standard methods.

The highest TPC, antioxidant capacity and OSI were obtained in early harvested olive oil, using two-phase decanters for TPC and three-phase decanters for antioxidant capacity and OSI. A prediction model, including the free acidity, K232, TPC, C18:2, C18:0, tyrosol and apigenin, was obtained; it allowed to predict very highly significantly the OSI (p < 0.001). Apigenin, tyrosol and C18:2 recorded the highest standardized coefficients (ß^+= 0.35) and thus had the highest influence on OSI. As per antioxidant capacity of olive oil, another very highly statistically significant prediction model was constructed (p < 0.001). It included only two predictors, oleacein and TPC, with the latter having the most influence (ß^+= 0.37).

The overall results highlighted the detrimental effects of agro-industrial factors on olive oil chemical composition, and this contributes significantly to improve olive oil’s quality and characteristics, which are important for the product economical and nutritional values.