To coordinate the supply chain risk caused by demand uncertainty, this paper proposed a flexible return strategy under demand uncertainty, in which the retailer can choose return quantity independently by put option after the selling season, while the return quantity is usually determined by the supplier in the classical return strategy. In our novel return strategy, the exercise price is not fixed, and we developed the base model of this strategy, named the selective buyback contracts model. We have solved the optimal pricing and ordering strategies of supply chain members. Numerical studies demonstrated that the contracts can coordinate a supply chain with one retailer and one supplier, and the supplier can adjust the profit distribution of the supply chain by adjusting the option exercise price. Compared with other return strategies, the selective buyback contracts give the retailer more power of choice, and the supplier receives risk compensation from the put options.

The evaluation of entrepreneurs’ uncertainty tolerance (UT) is more meaningful, e.g., predicting their behaviors, making psychological counseling strategies for them, etc. In fact, it is an uncertain problem that simultaneously contains randomness and fuzziness in evaluating entrepreneurs’ UT. Hence, it is difficult to solve it by traditional evaluation methods. This paper presents a cloud model-based comprehensive evaluation method for entrepreneurs’ UT which overcomes the inability of other methods to take into account randomness and fuzziness simultaneously. First, five UT levels are divided. Then, an evaluation index system which contains 14 secondary level indexes and four primary level indexes is constructed, and an analytic hierarchy process (AHP) method is used to obtain the weights for the secondary level indexes. Subsequently, the backward cloud generator (BCG) and virtual cloud model (CM) are used to obtain the CM of evaluation indexes, and cloud chart of evaluation results are generated by forward cloud generator (FCG). Finally, five core start-up founders of a high-tech company in China are chosen for a case study to illustrate our method. Compared with other traditional evaluation methods, our method has been verified to be a more competitive method. Its results are more visualized and low information lost.

Family farmers face various business risks, including natural disasters, policies, technology adoption, and market uncertainties. This paper develops a conceptual framework for the formation mechanism of family farmers’ business adaptation behaviors by linking “risk perception, key beliefs, and adaptation behavior”. Using microdata from 363 family farmers in Sichuan Province, China, and applying ordered logit model regression analysis, we find that both risk perception and key beliefs positively influence family farmers’ business adaptation behavior. Key beliefs partially mediate this relationship, while risk preference negatively moderates the effect of risk perception on business adaptation behavior. Additionally, family farmers with higher farm profit levels and larger social networks are more likely to engage in business adaptation behavior. Family farmers who have higher education, are older, operate larger-scale farms, and are male exhibit stronger risk perceptions.

Chinese family farms are continuously expanding; they are also facing various business risks that lead to a shorter lifespan. This paper constructed a family farm business risk assessment model that combined a hesitant fuzzy linguistic term sets (HFLTS) model with a hesitant fuzzy weighted average (HFWA) operator. On the basis of the factor analysis, this study built a family farm indicator system that included the natural, technical, market, policy, society, and management risk. The HFLTS was used for the assessment of weights in pairwise comparison matrices, and the HFWA operator was used as an aggregation operator to calculate the business risk score of family farms. For our case study, a method comparison analysis was also performed to check the validity of the results obtained by our risk assessment model.