Analysis of Consumer Preference for Green Tea with Eco-Friendly Certification in China

The eco-friendly certification system is designed to ensure safe agricultural products to consumers while minimizing environmental pollution. However, despite its advantages, it is not widely adopted due to a possible decrease of farmers’ income. In order to provide implication for activating the eco-friendly certification system, this paper examines the attributes of green tea which affect consumers’ preferences and estimates consumers’ willingness to pay (WTP) for the eco-friendly certification in China. A choice experiment survey is employed for data collection, and the random utility model is used to estimate the preference for the certification and quality of green tea. The attribute that yields the highest marginal WTP turns out to be the organic certification for which WTP is $115.9/250g higher than for no certification. Also, the analytical results indicate that the group with high trust is willing to pay up to $214.6/250g more for green tea with organic certification compared to the one with no certification. The empirical results suggest that it is important to build the consumers’ awareness and trust toward the certification to activate the eco-friendly certification system.

Discrete Choice Prox-Functions on the Simplex

We derive new prox-functions on the simplex from additive random utility models of discrete choice. They are convex conjugates of the corresponding surplus functions. In particular, we explicitly derive the convexity parameter of discrete choice prox-functions associated with generalized extreme value models, and specifically with generalized nested logit models. Incorporated into subgradient schemes, discrete choice prox-functions lead to a probabilistic interpretations of the iteration steps. As illustration, we discuss an economic application of discrete choice prox-functions in consumer theory. The dual averaging scheme from convex programming adjusts demand within a consumption cycle.

Mixed logit model based on nonlinear random utility functions: A transfer passenger demand prediction method on overnight D-trains

In recent years, with the development of high-speed railway in China, the operating mileage and passenger transport capacity have increased rapidly. Due to the high density of trains in the daytime, we usually set up skylights at night within 0:00-6:00 on high-speed railway for comprehensive maintenance, which contradict with the operation demand of D-series overnight high-speed trains (overnight D-trains for short). In order to dynamically adjust the operation plan of overnight D-trains with skylights coordinately, it is necessary to predict the passenger demand of newly-added overnight D-trains. Therefore, the purpose of this paper is to predict transfer passenger demand by formulating a mixed logit model based on nonlinear random utility functions for different transport modes. Firstly according to Maximum Simulated Likelihood Method, the likelihood function of this mixed logit model is proposed to maximize the overall utility value of different passenger groups. And then we adopt Metropolis-Hastings Algorithm to iteratively solve the probabilities of discrete random variables in utility functions. After that, we estimate the unknown distributions of elements in parameter vectors and solve the optimal solution of this model by traditional algorithms, basic heuristic algorithms and improved heuristic algorithms including Imporved Fireworks-Simulated Annealing Algorithm which is proposed in this paper, respectively. Finally, a real-world instance with related data of Beijing-Shanghai corridor, is implemented to demonstrate the performance and effectiveness of the proposed approaches.

What Is the Impact of Nonrandomness on Random Choice Models?

Problem definition: This paper examines the impact of nonrandomness on random choice models and studies various operations problems under the new discrete choice models. Academic/practical relevance: The literature often assumes that the random utility components follow some independent and identically distributed distribution. This assumption is too restrictive in some real-world scenarios, because, for example, consumers may have known well about the attribute values for the product that they have repeatedly purchased. Methodology: We adopt the random utility maximization framework and characterize the choice probabilities when the utility of some alternative is deterministic. The log-likelihood function is jointly concave in the attribute coefficients under the linear utility-attribute assumption; an expectation-maximization algorithm is developed to overcome the missing data issue in estimation. Results: Surprisingly, if the utility of a particular product is deterministic, the assortment problem is still polynomial-time solvable, whereas if the utility of the no-purchase option is deterministic, the decision problem corresponding to the assortment optimization is NP-complete. We show that the price minus the reciprocal of price sensitivity is product invariant at optimality, which helps to simplify the multiproduct pricing problems. Managerial implications: Empirical study on real data shows that incorporating nonrandomness into random choice models can increase model fitting and prediction accuracy. Failure of accounting for the impact of nonrandomness may result in substantial losses.