基于logit回归模型的顾客资产驱动因素与忠诚意向关系实证研究

国内关于顾客资产驱动因素模型的实证研究较少.将顾客资产三大驱动因素(价值资产、品牌资产和关系资产)与忠诚意向联系起来,用我国银行业688个数据进行了Logit回归建模,研究顾客资产驱动因素分别与忠诚意向之间的关系、驱动因素两两之间的关系以及顾客资产驱动因素组合对忠诚意向产生的协同效应.研究结论表明,首先,价值资产、品牌资产、关系资产均对顾客忠诚意向产生显著正影响;其次,品牌资产对关系资产和价值资产均产生显著正影响,但价值资产对关系资产的直接影响不显著,这可能是因为品牌资产在价值资产和关系资产之间的中介作用;最后,同时投资价值资产和品牌资产或同时投资关系资产和品牌资产反而会使顾客忠诚意向降低.研究结论能够为银行管理者优化营销经费配置提供理论支持.     

品牌资产在营销活动对顾客忠诚影响中的作用研究

在如今竞争越来越激烈的市场上,品牌资产越来越被学者与实业者所重视.同时,顾客忠诚作为企业获利的根本要素,也是关注的焦点.目前,企业开展营销活动是获取顾客忠诚的重要手段之一,但品牌资产在这一过程中所可能起到的作用的研究仍然较为缺乏.通过构建理论模型与实践调研对这一问题进行了实证研究,结果表明品牌资产的强度影响着营销活动对顾客忠诚的影响.最后在实证结论的基础上提出了相关管理启示.     

基于顾客资产驱动因素与同辈影响的青少年购买行为研究

研究的主要内容围绕着顾客资产驱动因素及同辈影响对青少年消费者购买意向和实际购买行为的影响展开.在文献综述的基础上,以运动品牌为例,分析价值资产、品牌资产、关系资产以及同辈影响对于青少年消费者购买意向的影响.实证研究表明,同辈影响对于青少年消费者购买意向有着非常显著的影响,同辈群体的炫耀、谈论与推荐行为越多,则青少年消费者的购买意向越强烈.与此同时,购买意向对于实际购买行为的影响虽是正向且显著地,但受到购买惯性的影响而减弱.结论一定程度上从统计上论证了青少年消费者的购买特点,同时也为运动品牌的青少年产品营销活动提供了参考依据.     

品牌资产与品牌价值关联模型的实证研究

现如今,品牌资产与品牌价值在企业经济活动中扮演的角色日益重要,二者的概念、特征以及作用机制等越来越被学者和企业界所重视,但聚焦品牌资产与品牌价值关联关系的研究仍然较为缺乏.通过对品牌资产与品牌价值的概念界定,依据品牌资产的作用机制和品牌价值的形成机理,构建了二者的关联理论模型,并通过实践调研对这一问题进行了实证研究,结果表明品牌资产通过调节效应能够驱动品牌价值的形成,最后为企业如何杠杆品牌资产实现品牌价值提出了相关管理启示.     

纵向数据部分线性测量误差模型的二次推断函数估计

基于纵向数据部分线性测量误差模型, 研究了模型中兴趣参数部分回归系数的估计问题. 首先采用B样条方法逼近模型中的非参数函数, 然后提出修正的二次推断函数(QIF)方法对模型中参数部分的回归系数进行估计, 所提方法可以提高估计的效率. 在一定的正则条件下, 证明了所得到的估计量具有相合性和渐近正态性. 最后, 通过模拟研究和实例分析验证了所提出估计方法的有限大样本性质.     

On sparse estimation for semiparametric linear transformation models

Semiparametric linear transformation models have received much attention due to their high flexibility in modeling survival data. A useful estimating equation procedure was recently proposed by Chen et al. (2002) [21] for linear transformation models to jointly estimate parametric and nonparametric terms. They showed that this procedure can yield a consistent and robust estimator. However, the problem of variable selection for linear transformation models has been less studied, partially because a convenient loss function is not readily available under this context. In this paper, we propose a simple yet powerful approach to achieve both sparse and consistent estimation for linear transformation models. The main idea is to derive a profiled score from the estimating equation of Chen et al. [21], construct a loss function based on the profile scored and its variance, and then minimize the loss subject to some shrinkage penalty. Under regularity conditions, we have shown that the resulting estimator is consistent for both model estimation and variable selection. Furthermore, the estimated parametric terms are asymptotically normal and can achieve a higher efficiency than that yielded from the estimation equations. For computation, we suggest a one-step approximation algorithm which can take advantage of the LARS and build the entire solution path efficiently. Performance of the new procedure is illustrated through numerous simulations and real examples including one microarray data.     

Measuring brand equity in the car market: a hedonic price analysis

This paper designs and implements a hedonic model to investigate brand-name effects in the car market. The proposed model provides considerable empirical evidence in support of model-name premia, after controlling for observed product differentiation. Such premia incorporate not only manufacturer equity but also effects that are specific to individual models. The estimated price premia are remarkably intuitive and consistent across carmakers and models. The results yield important implications for brand and range management in the car market and suggest directions for future research.     

基于改进营销收益模型的顾客资产测量——在银行业的应用研究

测量顾客资产是提升顾客资产的基础和前提。为实现对顾客资产的有效测量,本研究在顾客资产驱动要素模型的四维构成基础上,对顾客资产测量的营销收益模型进行了改进,并通过建立计量经济模型解决了需求市场规模变化问题,最后应用主成分分析和Logistic回归分析对顾客资产进行计算。在银行业的应用研究结果表明,改进后的模型能够考虑需求市场规模变化、交叉购买和口碑宣传等要素对顾客资产价值的影响,而且操作简单,可以通过统计软件完成全部计算过程。     

Pricing and Advertising of Private and National Brands in a Dynamic Marketing Channel

We consider a marketing channel where a retailer sells, along the manufacturer’s brand, its own store brand. We assume that each player invests in advertising in order to build the brand’s goodwill. One distinctive feature of this paper is the introduction of the negative effect of own advertising on other player’s goodwill stock evolution. We characterize feedback-Nash pricing and advertising strategies and assess the impact of the store brand and national brand’s goodwill stocks on these strategies in different settings. The main findings suggest first that investing in building up some equity for each brand reduces the price competition between them and propels the market power for both. Second, the retailer will pass to consumer an increase in its purchasing cost of the national brand in all situations as no coordination is taken into account to counter the double marginalization problem. Finally, the higher the brand equity of the store brand, the more the retailer invests in advertising.     

Effects of measurement error on a class of single-index varying coefficient regression models

This paper investigates the estimation in a class of single-index varying coefficient regression model when some covariates are contaminated with measurement errors. A bias-corrected least square procedure based on the observed data is proposed. By replacing the nonparametric single index part with a local linear approximation, an iterative algorithm for estimating the index parameter is proposed. More importantly, a special case is identified in which the naive procedure provides consistent estimates for the single index parameters. Large sample properties of the proposed estimators are established. The finite sample performance of the proposed estimators are evaluated by simulation studies.     

Measuring the effects of new brand introduction on inter-brand strategic interaction

This paper empirically investigates the effects of a new brand introduction on the market structure of a product market, i.e., the nature of competition among existing brands. The model is based on the discrete choice multinomial logit model in which the household-specific brand intercepts are decomposed into brand locations in attribute space and households' importance weights for these attributes. The formulation also incorporates the effects of marketing variables on brand choice behavior. The relative proximity of brands in the attribute space provides a measure of the intensity of competitive rivalry among brands. A new brand introduction results in an additional location in this space. Three consequences of the introduction are investigated. (i) Impact on brand locations of extant brands. (ii) Change in the importance weights assigned by households to the attributes. (iii) Effects on households' sensitivities to marketing activities. As some or all of the above could change, these three effects result in eight combinations that need to be studied. The general model is one in which all three components are allowed to change and this nests the remaining seven specifications. The specification that is most consistent with the data can, therefore, be isolated via a statistical test of nested hypotheses. Identifying the consequences of the introduction for market structure has implications for managerial action. An empirical application of the model to household scanner panel data using liquid laundry detergents is provided.     

消费者心理违约对品牌权益的影响机制研究

本文旨在把组织行为学中的心理契约理论应用于市场营销情景下的品牌关系。本文通过把心理违约分成两个阶段,即心理违约(Psychological Contrac(?) Breach)和心理违背(Psychological Contract Violation)来研究消费者心理违约对品牌权益的影响。结果显示,心理违背会负面地影响品牌形象和品牌忠诚。但在心理违约和心理违背之间还存在着其它影响因素,有待进一步的研究。     

Semiparametric estimation of average treatment effect through a random coefficient dummy endogenous variable model

This paper provides an estimation procedure for average treatment effect through a random coefficient dummy endogenous variable model. A leading example of the model is estimating the effect of a training program on earnings. The model is composed of two equations: an outcome equation and a decision equation. Given the linear restriction in outcome and decision equations, Chen (1999) provided a distribution-free estimation procedure under conditional symmetric error distributions. In this paper we extend Chen’s estimator by relaxing the linear index into a nonparametric function, which greatly reduces the risk of model misspecification. A two-step approach is proposed: the first step uses a nonparametric regression estimator for the decision variable, and the second step uses an instrumental variables approach to estimate average treatment effect in the outcome equation. The proposed estimator is shown to be consistent and asymptotically normally distributed. Furthermore, we investigate the finite performance of our estimator by a Monte Carlo study and also use our estimator to study the return of college education in different periods of China. The estimates seem more reasonable than those of other commonly used estimators.     

ZIPMAP — A Zero-One Integer Programming Model for Market Segmentation and Product Positioning

An optimization model is proposed to aid marketing managers to search for and develop new product brand ideas. The model, which is founded on individual consumer behaviour constructs, determines a position for a new brand, in the consumers' perceptual space of product attributes, that maximizes company sales. The resulting mathematical model is stated as a large-scale, mixed, zero-one, integer, non-linear mathematical programming problem whose solution is sought through a two-stage optimization approach.     

Autoregressive process modeling via the Lasso procedure

The Lasso is a popular model selection and estimation procedure for linear models that enjoys nice theoretical properties. In this paper, we study the Lasso estimator for fitting autoregressive time series models. We adopt a double asymptotic framework where the maximal lag may increase with the sample size. We derive theoretical results establishing various types of consistency. In particular, we derive conditions under which the Lasso estimator for the autoregressive coefficients is model selection consistent, estimation consistent and prediction consistent. Simulation study results are reported.     

Asymptotically efficient parameter estimation for ordinary differential equations

Parameter estimation for ordinary differential equations arises in many fields of science and engineering. To be the best of our knowledge, traditional methods are often either computationally intensive or inaccurate for statistical inference. Ramsay et al. (2007) proposed a generalized profiling procedure. It is easily implementable and has been demonstrated to have encouraging numerical performance. However, little is known about statistical properties of this procedure. In this paper, we provide a theoretical justification of the generalized profiling procedure. Under some regularity conditions, the procedure is shown to be consistent for a broad range of tuning parameters. When the tuning parameters are sufficiently large, the procedure can be further shown to be asymptotically normal and efficient.     

Optimal learning for sequential sampling with non-parametric beliefs

We propose a sequential learning policy for ranking and selection problems, where we use a non-parametric procedure for estimating the value of a policy. Our estimation approach aggregates over a set of kernel functions in order to achieve a more consistent estimator. Each element in the kernel estimation set uses a different bandwidth to achieve better aggregation. The final estimate uses a weighting scheme with the inverse mean square errors of the kernel estimators as weights. This weighting scheme is shown to be optimal under independent kernel estimators. For choosing the measurement, we employ the knowledge gradient policy that relies on predictive distributions to calculate the optimal sampling point. Our method allows a setting where the beliefs are expected to be correlated but the correlation structure is unknown beforehand. Moreover, the proposed policy is shown to be asymptotically optimal.     

新媒体环境中品牌粉丝效应形成机理研究

随着新媒体技术的不断发展以及消费模式的变迁,电子商务呈现出新型业态,对品牌新媒体营销提出了新的挑战。消费者的品牌消费已经从传统的功能性需求转移到更多的价值需求,营销领域的“粉丝效应”应运而生。本文即研究新媒体环境中粉丝效应的形成机理。结合新媒体环境,文中描述了粉丝效应的概念及内涵,探讨了新媒体环境中新型电商的业态及价值产出模式的变革,提出了粉丝效应的实现过程并实证分析了粉丝效应的形成机理。文中提出粉丝效应实现的三种影响因素,包括品牌体验、品牌认同、品牌形象。研究结果表明,品牌体验对品牌认同有正向影响;品牌认同分为个人品牌认同及社会品牌认同,个人品牌认同对社会品牌认同有正向影响;品牌认同对品牌形象有正向影响;品牌认同和品牌形象均对品牌粉丝效应有正向影响。     

Estimating structural credit risk models when market prices are contaminated with noise

In this paper, sequential estimation on hidden asset value and model parameter estimation is implemented under the Black–Cox model. To capture short‐term autocorrelation in the stock market, we assume that market noise follows a mean reverting process. For estimation, Bayesian methods are applied in this paper: the particle filter algorithm for sequential estimation of asset value and the generalized Gibbs and multivariate adapted Metropolis methods for model parameters estimation. The first simulation study shows that sequential hidden asset value estimation using both option price and equity price is more efficient than estimation using equity price alone. The second simulation study shows that, by applying the generalized Gibbs sampling and multivariate adapted Metropolis methods, model parameters can be estimated successfully. In an empirical analysis, the stock market noise for firms with more liquid stock is estimated as having smaller volatility. Copyright © 2015 John Wiley & Sons, Ltd.     

Local Walsh-average-based estimation and variable selection for single-index models

We propose a robust estimation procedure based on local Walsh-average regression(LWR) for single-index models. Our novel method provides a root-n consistent estimate of the single-index parameter under some mild regularity conditions; the estimate of the unknown link function converges at the usual rate for the nonparametric estimation of a univariate covariate. We theoretically demonstrate that the new estimators show significant efficiency gain across a wide spectrum of non-normal error distributions and have almost no loss of efficiency for the normal error. Even in the worst case, the asymptotic relative efficiency(ARE) has a lower bound compared with the least squares(LS) estimates; the lower bounds of the AREs are 0.864 and 0.8896 for the single-index parameter and nonparametric function, respectively. Moreover, the ARE of the proposed LWR-based approach versus the ARE of the LS-based method has an expression that is closely related to the ARE of the signed-rank Wilcoxon test as compared with the t-test. In addition, to obtain a sparse estimate of the single-index parameter, we develop a variable selection procedure by combining the estimation method with smoothly clipped absolute deviation penalty; this procedure is shown to possess the oracle property. We also propose a Bayes information criterion(BIC)-type criterion for selecting the tuning parameter and further prove its ability to consistently identify the true model. We conduct some Monte Carlo simulations and a real data analysis to illustrate the finite sample performance of the proposed methods.