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Register a new userInfluencing factors that determine the usage of the crowd-shipping services
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The objective of this study is to understand how senders choose shipping services for different products, given the availability of both emerging crowd-shipping (CS) and traditional carriers in a logistics market. Using data collected from a US survey, Random Utility Maximization (RUM) and Random Regret Minimization (RRM) models have been employed to reveal factors that influence the diversity of decisions made by senders. Shipping costs, along with additional real-time services such as courier reputations, tracking info, e-notifications, and customized delivery time and location, have been found to have remarkable impacts on senders choices. Interestingly, potential senders were willing to pay more to ship grocery items such as food, beverages, and medicines by CS services. Moreover, the real-time services have low elasticities, meaning that only a slight change in those services will lead to a change in sender-behavior. Finally, data-science techniques were used to assess the performance of the RUM and RRM models and found to have similar accuracies. The findings from this research will help logistics firms address potential market segments, prepare service configurations to fulfill senders expectations, and develop effective business operations strategies.
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The study examines the relationship between mobile financial services and individual financial behavior in India wherein a sizeable population is yet to be financially included. Addressing the endogeneity associated with the use of mobile financial services using an instrumental variable method, the study finds that the use of mobile financial services increases the likelihood of investment, having insurance and borrowing from formal financial institutions. Further, the analysis highlights that access to mobile financial services have the potential to bridge the gender divide in financial inclusion. Fastening the pace of access to mobile financial services may partially alter pandemic induced poverty.
The objective of this study is to understand the different behavioral considerations that govern the choice of people to engage in a crowd-shipping market. Using novel data collected by the researchers in the US, we develop discrete-continuous models. A binary logit model has been used to estimate crowd-shippers willingness to work, and an ordinary least-square regression model has been employed to calculate crowd-shippers maximum tolerance for shipping and delivery times. A selectivity-bias term has been included in the model to correct for the conditional relationships of the crowd-shippers willingness to work and their maximum travel time tolerance. The results show socio-demographic characteristics (e.g. age, gender, race, income, and education level), transporting freight experience, and number of social media usages significant influence the decision to participate in the crowd-shipping market. In addition, crowd-shippers pay expectations were found to be reasonable and concurrent with the literature on value-of-time. Findings from this research are helpful for crowd-shipping companies to identify and attract potential shippers. In addition, an understanding of crowd-shippers - their behaviors, perceptions, demographics, pay expectations, and in which contexts they are willing to divert from their route - are valuable to the development of business strategies such as matching criteria and compensation schemes for driver-partners.
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Leysan Anvarovna Davletshina
, Natalia Alekseevna Sadovnikova
, n Alexander Valeryevich Bezrukov
2021
The article analyzes the populations assessment of their own health and attitude to a healthy lifestyle in the context of distribution by age groups. Of particular interest is the presence of transformations taking into account the complex epidemiological situation, the increase in the incidence of coronavirus infection in the population (the peak of the incidence came during the period of selective observation in 2020). The article assesses the closeness of the relationship between the respondents belonging to a particular socio-demographic group and their social well-being during the period of self-isolation, quarantine or other restrictions imposed during the coronavirus pandemic in 2020. To solve this problem, the demographic and socio-economic characteristics of respondents are presented, the distribution of responses according to the survey results is estimated and the most significant factor characteristics are selected. The distributions of respondents responses are presented for the selected questions. To determine the closeness of the relationship between the respondents answers to the question and their gender or age distribution, the coefficients of mutual conjugacy and rank correlation coefficients were calculated and analyzed. The ultimate goal of the analytical component of this study is to determine the social well-being of the Russian population during the pandemic on the basis of sample survey data. As a result of the analysis of changes for the period 2019-2020, the assessment of the closeness of communication revealed the parameters that form differences (gender, wealth, territory of residence).
We develop an agent-based simulation of the catastrophe insurance and reinsurance industry and use it to study the problem of risk model homogeneity. The model simulates the balance sheets of insurance firms, who collect premiums from clients in return for ensuring them against intermittent, heavy-tailed risks. Firms manage their capital and pay dividends to their investors, and use either reinsurance contracts or cat bonds to hedge their tail risk. The model generates plausible time series of profits and losses and recovers stylized facts, such as the insurance cycle and the emergence of asymmetric, long tailed firm size distributions. We use the model to investigate the problem of risk model homogeneity. Under Solvency II, insurance companies are required to use only certified risk models. This has led to a situation in which only a few firms provide risk models, creating a systemic fragility to the errors in these models. We demonstrate that using too few models increases the risk of nonpayment and default while lowering profits for the industry as a whole. The presence of the reinsurance industry ameliorates the problem but does not remove it. Our results suggest that it would be valuable for regulators to incentivize model diversity. The framework we develop here provides a first step toward a simulation model of the insurance industry for testing policies and strategies for better capital management.
This paper studies reputation in the online market for illegal drugs in which no legal institutions exist to alleviate uncertainty. Trade takes place on platforms that offer rating systems for sellers, thereby providing an observable measure of reputation. The analysis exploits the fact that one of the two dominant platforms unexpectedly disappeared. Re-entering sellers reset their rating. The results show that on average prices decreased by up to 9% and that a 1% increase in rating causes a price increase of 1%. Ratings and prices recover after about three months. We calculate that identified good types earn 1,650 USD more per week.
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