Regret and Satisfaction as Determinants of Lapsed Donor Recommencement Decisions

Although a large amount of research has been undertaken into donor acquisition, relationship development, and the reasons why certain donors terminate their support for fundraising charities, few studies have examined the factors that encourage lapsed donors to resume giving to the organizations they have deserted. This empirical investigation sought to contribute to contemporary knowledge concerning this important matter via a survey of 310 lapsed donors (some of whom had resumed giving) to a hospice charity in the south east of England. The variables hypothesized to influence donor revival decisions comprised a person's satisfaction with the charity's work, past communications received from the organization, communications associated with the charity's revival efforts, the individual's donation history and reason for initial lapse, and the ex‐donor's degree of involvement with charity giving. A person's feelings of regret were posited to represent a critical mediating variable between several of the abovementioned factors and a lapsed donor's decision to resume or not to resume giving. The results suggest that regret did indeed play a major role in lapsed donors' revival processes and that an individual's satisfaction with the quality of a charity's communications requesting the recommencement of the individual's support was a crucial determinant of restart decisions.     

反反悔机制的建构与社会信用的维系

信用是涉及至少两个行为方的一种约定关系,约定的遵守意味着信用的建立。在具有交互性的行为中,策略选择或均衡的不确定性会促使各行为方建立约定,实施一种确定的策略选择,以确保他们不陷入最糟糕的境地。有些行为方有违反约定的反悔行为,以便使策略选择从一种均衡走向另一种均衡,提高自己的收益。违约现象的出现会影响到社会信用的建构。在反反悔机制下,意欲反悔者无法实施其反悔行为,行为各方均按照约定行事,社会信用能够得以维系、建构。     

Minimax estimators andΓ-minimax estimators for a bounded normal mean under the lossl p (θ, d)=|θ-d|p

Summary Let the random variableX be normal distributed with known varianceσ ²>0. It is supposed that the unknown meanθ is an element of a bounded intervalΘ. The problem of estimatingθ under the loss functionl _p (θ, d)=|θ-d| ^p p≥2 is considered. In case the length of the intervalθ is sufficiently small the minimax estimator and theΓ(β, τ)-minimax estimator, whereΓ(β, τ) represents special vague prior information, are given.     

Adverse selection under ignorance

Summary. We examine an adverse selection relationship in which the principal may ignore the ex ante distribution of the agent's types. The principal's behavior is described by a disutility function that covers the standard minimax regret and minimax loss criteria. We show that the incentive compatible and individually rational mechanism, which minimizes the maximal (or the minimal) principal's disutility over a set of priors, requires the efficient agents to realize the corresponding first-best actions and may demand actions lower than the first-best ones from less efficient agents. We also analyze the qualitative differences between the case in which the principal considers regrets and the case in which he considers losses. Received: 19 October 1998; revised version: 9 November 1999     

Minimax estimation of a cumulative distribution function by converting to a parametric problem

Let X = (X ₁,...,X _n ) be a sample from an unknown cumulative distribution function F defined on the real line . The problem of estimating the cumulative distribution function F is considered using a decision theoretic approach. No assumptions are imposed on the unknown function F. A general method of finding a minimax estimator d(t;X) of F under the loss function of a general form is presented. The method of solution is based on converting the nonparametric problem of searching for minimax estimators of a distribution function to the parametric problem of searching for minimax estimators of the probability of success for a binomial distribution. The solution uses also the completeness property of the class of monotone decision procedures in a monotone decision problem. Some special cases of the underlying problem are considered in the situation when the loss function in the nonparametric problem is defined by a weighted squared, LINEX or a weighted absolute error.     

Robust upper bounds for American put options

In this paper, we develop robust and model-free upper bounds for American put option prices. Our bounds have all of those appealing features of the upper bounds for European options provided in DeMarzo et al. (2016, Robust option pricing: Hannan and Blackwell meet Black and Scholes, Journal of Economic Theory, 410-434) but cover more popular derivatives in practice. Numerical and empirical investigations illustrate the performance of our method.     

A Linear Programming approach for robust network revenue management in the airline industry

The classical revenue management problem consists of allocating a fixed network capacity to different customer classes, so as to maximize revenue. This area has been widely applied in service industries that are characterized by a fixed perishable capacity, such as airlines, cruises, hotels, etc.It is traditionally assumed that demand is uncertain, but can be characterized as a stochastic process (See Talluri and van Ryzin (2005) for a review of the revenue management models). In practice, however, airlines have limited demand information and are unable to fully characterize demand stochastic processes. Robust optimization methods have been proposed to overcome this modeling challenge. Under robust optimization framework, demand is only assumed to lie within a polyhedral uncertainty set (Lan et al. (2008); Perakis and Roels (2010)).In this paper, we consider the multi-fare, network revenue management problem for the case demand information is limited (i.e. the only information available is lower/upper bounds on demand). Under this interval uncertainty, we characterize the robust optimal booking limit policy by use of minimax regret criterion. We present an LP (Linear Programming) solvable mathematical program for the maximum regret so our model is able to solve large-scale problems for practical use. A genetic algorithm is proposed to find the booking limit control to minimize the maximum regret. We provide computational experiments and compare our methods to existing ones. The results demonstrate the effectiveness of our robust approach.     

旅游后悔心理的后续行为表现研究

后悔是全面解释消费者满意度和后续行为意图的重要因素.本文首先梳理了消费者后悔心理的研究成果,并提出了旅游者后悔心理的基本定义和成因.在此基础上,对实证调查资料使用因子分析法总结出旅游后悔心理的后续行为表现为5类:采取相对激烈的对抗行为、通过网络负面宣传、向旅游管理机关投诉、断绝该地旅游并影响亲友和借鉴经历调整心态.单因素方差分析显示:男性较女性更容易"采取相对激烈的对抗行为"、"通过网络负面宣传"及"向旅游管理机关投诉"方式表达自己的旅游后悔心理;个体所学科别的差异对采取这5种行为方式来表达旅游后悔心理没有明显的差异.本研究成果在一定程度上弥补了旅游后悔心理实证研究的不足,为旅游目的地经营者有效疏导旅游后悔心理提供了战略层面的参考.     

Robustness of simple menus of contracts in cost-based procurement

We consider a model of cost-based procurement in which the principal faces Knightian uncertainty about the agent's preferences for cost reduction. We show that a particularly simple incentive scheme—a menu comprising a fixed-price contract and a cost-reimbursement contract—minimizes the maximum expected payment, where this maximum is taken over the set of possible agent preferences. For some parameters of the problem, a range of alternative incentive schemes also satisfy this criterion. We show that the simple incentive scheme is not weakly dominated by any of the alternatives: there does not exist an alternative mechanism for which the expected payment is no higher for all realizations of the agent's preferences and strictly lower for some realization.     

The mixture index of fit and minimax regression

A measure of the fit of a statistical model can be obtained by estimating the relative size of the largest fraction of the population where a distribution belonging to the model may be valid. This is the mixture index of fit that was suggested for models for contingency tables by Rudas, Clogg, Lindsay (1994) and it is extended here for models involving continuous observations. In particular, the approach is applied to regression models with normal and uniform error structures. Best fit, as measured by the mixture index of fit, is obtained with minimax estimation of the regression parameters. Therefore, whenever minimax estimation is used for these problems, the mixture index of fit provides a natural approach for measuring model fit and for variable selection. Received: September 1997