Suchergebnisse

Individuals' self-control resource (SCR) determines the strength of their self-control, which affects risk preference in risky choice decision making. However, the role of SCR in evaluating risk preference is poorly understood. I designed 2 studies consisting of a laboratory experiment
in which participants were required to choose from 2 options in each of 4 risky-choice scenarios, to examine how variance in individuals' SCR level affected their risk preference. Results indicated that individuals showed a stronger risk preference when they had a higher SCR level than when
their SCR level was lower and that they showed less risk preference when their level of SCR was depleted. In accordance with these findings, I investigated the mechanisms of SCR level and variance and discussed the role of SCR in risky choice decision making.

AbstractAs the core element of technological innovation, financial resources can play the role of financial support, risk sharing, service guidance, and other aspects, thereby enhancing the efficiency of sci‐tech innovation (STIE). Whether there is a complex nonlinear economic threshold effect of financial development (FD) on STIE in different regional context is worth further discussion. This paper selects the Yangtze River Economic Belt (YREB) of China as the research object, based on the panel data of 110 cities, uses the Hansen threshold model to test the nonlinear "economic threshold effect" of FD on STIE. The results show that under different development scenarios in different regions, FD has a nonlinear "economic threshold effect" on STIE. This study extends the research sample to the cities of river basin, and elucidating the importance of regional context to the nonlinear "economic threshold effect" of FD on STIE further proposes that the sole focus of promoting the quantity of technology innovation by taking advantage of FD do not guarantee a positive effect on the development of STIE. Instead, the diverse, multiple financial policy instruments and stimulating the driving force of FD should be adopted towards the improvement of STIE in a phased and context specific manner.

Understanding the connections between the built environment and travel mode choice is a major research topic in transportation. However, existing studies usually examine the relationship through trip-based analyses rather than tour-based approaches. A tour consists of multiple trips that originate and end at the same place, which is increasingly considered the more appropriate analysis unit for travel behaviors. Applying a tour-based approach, this study employs random forest to investigate the non-linear impacts of built environment factors and tour attributes on different mode combinations of a tour. We find that tour attributes and connectivity-related variables (e.g., block size and intersection density) have a strong association with the use of active travel modes when their values are within a certain threshold. In addition, capturing mode change behaviors offers more nuanced understanding of how various built environment variables shape people's decision to combine modes in a tour.

An emerging voice of patients in the form of opinion and expectation about the quality of care has the potential for improvement in service quality. A large volume of patients' opinion as online doctor reviews are available online in order to access, analyze, and improve patients' perception. This paper aims to mine online doctor reviews to explore the different determinants of patient satisfaction and dissatisfaction toward the United Kingdom (U.K.) healthcare services with the two-factor theory. This study is based on the two-factor theory, in light of online doctor reviews from a prominent health rating website in the U.K (Iwantgreatcare.org), an improved text mining approach is proposed by introducing SenticNet to Latent Dirichlet Allocation (LDA) to analyze patients' healthcare experience. This method finds latent topics that create either patient satisfaction or dissatisfaction toward a healthcare system. We then calculate the patient satisfaction rate across each disease specialty. Then sentiment analysis was performed using the Hourglass of Emotions model to compute the polarity of each review sentence (positive, or negative). Next, the classification results reveal that the proposed model that analyzes patient opinion toward a different aspect of care outperformed other state-of-the-art models. Finally, a SWOT and root-cause analysis is performed to identify strengths, weaknesses, opportunities, and threats to a medical system. Our findings provide a clue for doctors, hospital, and government officials to enhance patient satisfaction and minimize dissatisfaction by satisfying their needs and improve the quality of care across different diseases. Key words: patient satisfaction; text mining; topic modeling; LDA; sentiment analysis.