毕业论文外文翻译-满意度强度和顾客忠诚度.doc
外文翻译之一Satisfaction Strength and Customer Loyalty作者： Murali Chandrashekaran /Kristin Rotte/ Stephen S. Tax/ Rajdeep Grewa国籍：Australia出处：Journal Of Marketing Research ,2006,5(9):1784-1789原文正文： AbstractAlthough empirical research indicates that satisfaction is intimately linked to loyalty, anecdotal evidence reveals that many customers who state that they are very satisfied with a service provider nevertheless subsequently defect. In this paper, the authors focus on identifying which customers are vulnerable to defection despite stating high levels of satisfaction. Drawing on the emerging perspective to modeling individual judgments that recognizes that individuals differ in the strength (i.e., conviction, certainty) with which judgments are professed, the authors first decompose a customers stated satisfaction into two related but independent facets satisfaction level and satisfaction strength The authors then examine the role of satisfaction strength in the translation of satisfaction to loyalty. Results from two studies are reported. In the first study, set in a B2B service context, the authors analyze data obtained from an ongoing customer satisfaction tracking study being conducted by a large service organization in the US. Data from over 25,000 customers are used to calibrate the satisfaction model and examine the effect of satisfaction strength on the translation of satisfaction to loyalty. In the second study, a conceptual replication set in a B2C context, the authors examine decision-making following a failed service encounter and a recovery attempt by the service provider. The authors study the impact of perceptions of service recovery on the level and strength of the stated satisfaction with the service recovery, and then focus on the effect of satisfaction strength in the translation of stated satisfaction to loyalty. The two studies strongly demonstrate that the covert satisfaction strength plays a central role in the translation of satisfaction to loyalty. A key finding that is uncovered, and replicated, in this research is that while satisfaction does indeed translate to loyalty when the satisfaction judgment is strongly-held (i.e., with low uncertainty), the translation is significantly lowered, on average, by almost 60%, when the same stated satisfaction is more weakly-held (i.e., fraught with uncertainty). The studies also indicate that aspects of prior relational experience (length of relationship, volume of business, and favorability of prior experiences) serve to isolate, rather than insulate, a firms customers, resulting in even greater vulnerability Overall, the findings contribute to a better understanding of the process by which satisfaction leads to customer loyaltyResearch FrameworkWe build on the extant satisfaction and judgment formation literatures to recognize that a customers overtly stated satisfaction is comprised of two related but distinct dimensions the level of satisfaction and the covert strength with which that satisfaction judgment is held. Several lines of thought support this two-dimensional conceptualization of revealed satisfaction. For instance,scholars in the area of services marketing note that customer expectations are often fuzzy (e.g., Rustet al 1999), and it is often difficult for customers to precisely estimate the level of received service (Parasuraman, Zeithaml and Berry 1985). Thus, it is likely that the resulting satisfaction judgments themselves are laden with uncertainty. Consequently, customers are likely to differ in the strength with which they hold their satisfaction. This is also consistent with the psychological view of human judgments succinctly expressed by Koehler (1994, p. 461): “Although we believe a great many things, we hold some of our beliefs with greater conviction than others.”Satisfaction modelLetting SATi denote the stated satisfaction of the its customer, we recognize that SATi is a realization from a distribution of possible judgments, such that SLi, the satisfaction level, reflects the mean, and SUi, the satisfaction uncertainty, manifests itself in the variance of that distribution:(1) SATi = 0 + SLi + i; var(i) = 2 + SUi(2) SLi = Xi; SUi = ZiWhere 2 denotes the measurement- and model-error variance; Xi = x1i,., xpi and Zi = z1i,., zki denote row-vectors of variables hypothesized to impact satisfaction level and uncertainty, respectively, and = 1, 2, ., p and = 1, 2, ., k denote column-vectors of the impacts of Xi and Zi, respectively. The specific elements of Xi and Zi will come from theory and the specific substantive setting of the particular research study. Consistent with the JUMP model procedure the parameters of interest can be estimated in a straightforward manner using feasible generalized least squares.Loyalty ModelResearch indicates that although satisfaction is linked to some aspects of loyalty (e.g.,Anderson and Sullivan 1993; Mittal and Kamakura 2001; Oliver 1997) its impact may depend on facets of the prior relational experience (e.g., Rust, Lemon and Zeithaml 2004). In addition, we anticipate that both loyalty and the translation of satisfaction to loyalty will be influenced by satisfaction strength.Role of satisfaction strength. We first anticipate that uncertainty in customer evaluations will hinder continued patronage (Kardes 1994). Next, drawing from research in psychology and marketing, we advance the notion that satisfaction strength will play an important role in the translation of stated satisfaction to loyalty. The specific conjecture that is widely believed in the psychology literature is that strongly-held judgments (i.e., those with little uncertainty) are more likely to translate into subsequent behavior (Gross, Miller and Holtz 1995; Kardes 1994). In a similar vein, Chandrashekaran et al (2000) found that intention uncertainty significantly lowered the translation of intention judgments to actual behavior. We therefore expect that the translation of stated satisfaction to loyalty will increase (decrease) as the satisfaction strength increases (decreases).Role of prior relational experiences insulation or isolation? It is generally believed that long-standing and happy customers are more loyal (e.g., more likely to provide recommendations and positive word-of-mouth; Zeithaml, Berry and Parasuraman 1996). We also expect prior relational experiences (duration, valence and business volume) to impact the translation of satisfaction to loyalty.Discussion and ConclusionThe key objectives of this paper were to examine if there is something in the measure ofsatisfaction itself that helps better illuminate the satisfaction-loyalty link. We advanced the view thatcstomer satisfaction can be constructively viewed as a two-dimensional statistical construct that mbodies both level and strength. In contrast to extant research that has largely focused on the level of satisfaction, we articulated a model of satisfaction that simultaneously assessed the impact of independent variables on both the level of satisfaction and the strength of satisfaction. We then theorized that weakly-held satisfaction would not translate to loyalty, and that only strongly-held satisfaction would be potent and translate to loyalty. We also examined how different aspects of prior relational experience (length of relationship, volume of business, and favorability of prior experience) influenced this translation process.We assessed our theorizing in the context of two studies, covering a range of market situations. In study 1, we centered on one B2B service provider whose customers came from a wide range of industries. In study 2, we focused on individual customer experiences with service providers from a variety of industries. In both studies, the results strongly demonstrate that the covert satisfaction strength assumes a central role in the translation of satisfaction to loyalty. A key finding that is uncovered, and replicated in this paper, is that while satisfaction does indeed translate to loyalty when the satisfaction judgment is strongly-held, the translation is significantly lowered, on average, by almost 60%, when the same satisfaction is more weakly-held. 满意度强度和顾客忠诚度作者：Murali Chandrashekaran/Kristin Rotte/Stephen S. Tax/Rajdeep Grewa国籍：澳大利亚出处：市场调查杂志,2006年5(9):1784-1789中文译文：摘要尽管经验上的调查显示满意度和忠诚度是息息相关的，而许多证据也表明了很多顾客对一些服务提供者刚开始提供服务的时候很满意，不过随后并没有坚持下去，而是出现了变化。本文，作者集中识别那些尽管对商品或服务有很高的满意度却仍然叛变，没有忠诚购买的客户。从新兴的视角去建立模型来做判断可得出识别个人认知的强度（如，信念，确定性）和他们所声称的判断力是不同的。作者首先把顾客所谓的满意度分成两个相关的但是又相互独立的方面满意度水平和满意度强度。作者接着检查了满意度强度在将满意度转化成忠诚度的时候所发挥的作用。而这个实验的结果由两个调查研究得到。第一个研究是建立在一个B2B的服务体系中的，作者从美国一个很大的服务组织获得数据并将这些数据作为研究来源，这个组织的数据是对在线购买的顾客满意度的追踪得到的。从超过25000个消费者中得到的数据显示，需要校正满意度模型和检查满意度强度在将满意度转化成忠诚度上所起的作用。第二个研究是从第一个研究中概念性的复制了原理及方法，并将环境设在了一个B2C的体系中，作者验证了消费者在一个失败的购物或体验后恢复对服务提供者的信任并愿意再次尝试购买所需要的条件。作者研究了对服务回收的认知水平和强度对服务满意度的影响，然后专注于满意度强度在将顾客满意度转化成顾客忠诚度的影响。这两项研究强烈的表明了，隐含的满意度强度在将满意度转化成忠诚度时候起到了一个中心的作用。一个关键的发现在研究中被揭示并可复制应用在不同环境的是，当对满意度的判断是在强烈一面（例如，低的不确定性）的时候，满意度确实能转化成忠诚度。而当这种满意度是在微弱的一面的时候（例如充满不确定性），那么满意度转化成忠诚度的水平就会降低，平均大概是60%。研究还表明前人相关经验（如关系的长度，营业额，先前经历的支持率）的影响是相对独立的，但并非完全隔离。一个公司的顾客可能会导致更加严重的价值缺失。总的来说，这些发现有助于更好的理解顾客的满意度转化成忠诚度的过程。研究框架我们在现存的满意度和形态判断的文献的基础上去认识一个顾客对产品和服务的明显满意度是由两个相关但是独立的因素构成的满意度的水平和满意度判断所依据的隐藏的强度。有很多想法支持这两个因素是满意度相关性的概念化。例如，在服务市场领域的学者指出顾客的期望往往是模糊的（例如，Rust在1999），并且对顾客来说精确的估计他们所获得的服务的水平经常是很困难的一件事（Parasuraman,Zeithaml,和Berry在1985）。因此，似乎导致满意度的判断本身即充满了不确定性。结果是，消费者更容易区分让他们保持满意度的强度。这也和Koehler(1994,p.461)表达的心理学上关于人类判断力的观点是一致的，他说：“尽管我们相信很多事情，我们仍然会觉得有比这些事情更好的事情”。 满意度模型设SATi 表示第i个顾客的满意度水平，我们认为SATi 是来源于可能判断的认识，比如满意度水平（简称SL）就反应了这个意思，而满意度不确定性（SU）表明它的分布变化本身：(1) SATi = 0 + SLi + i; var(i) = 2 + SUi(2) SLi = Xi; SUi = Zi当2 表示度量和模型错误的变化；Xi = x1i,., xpi 和Zi = z1i,., zki 分别表示假设影响满意度水平和不确实性水平的变化的横向量。而 = 1, 2, ., p 和 = 1, 2, ., k 分别表示影响Xi 和Zi, 的纵向量。Xi 和Zi 的特殊因素将来源于建立调查研究的理论和现实因素。利益的参数可以直接用公式来估算。忠诚度模型调查表明，尽管满意度在某些方面和忠诚度是息息相关的(例如，Anderson 和Sullivan 在1993年；Mittal和Kamakura在2001年；Oliver在1997年)，它的影响可能取决于先前的相关经验的方面（例如Rust,Lemon和Zeithaml在2004）。另外，我们预料忠诚度和将满意度转化成忠诚度都将受到满意度的强度影响。满意度强度的作用。我们首先假设不确定性在顾客对商品和服务质量的评估上将阻碍顾客继续光顾（Kardes在1994）。接着，从心理上和市场上的调查我们得出满意度强度在将满意度转化成忠诚度的过程中扮演了一个很重要的角色的见解。在心理学文献上被广泛信任的特殊观点是强烈的判断力（例如这些有很低的不确定性的）更可能将满意度转化成确切的行为（Gross, Miller 和 Holtz在1995；Kardes在1994）。类似的，Chandrashekaran 在2000发现隐藏的不确定性降低了将隐含的判断力转化成现实的行为的可能性。因此我们假设一定的满意度转化成忠诚度的程度将随着满意度强度的增加（减少）而增加（减少）。先前相关经历的作用隔离或孤立？人们通常认为长期存在并快乐的顾客会更加忠诚（例如，更可能向他人推荐和一些好的口碑，Zeithaml Berry 和Parasuraman在1996）我们也预料先前的相关经历（持续，价格，生意量）对满意度转化成忠诚度也会产生影响。讨论与结论 这篇文章的主要目标是去测试是否存在某些东西能够衡量满意度本身，并且能更好的帮助建立起满意度与忠诚度之间的联系。我们得出顾客的满意度能被建设性的认为是由满意度水平和满意度强度这两个因素共同作用的结果。我们明确的建立一个模型来仿真评估满意度水平和满意度强度所产生的独立的影响，而不是仅仅将调查集中在满意度水平。然后我们得出弱的满意度是不会转化成忠诚度的，只有强烈的满意度才会是永久的并且能转化成忠诚度。我们也测试了前人的不同的相关经验（关系的长度，顾客量，前人经验的支持率）对满意度转化成忠诚度的过程的影响。 我们对这两个研究的理论化的环境进行评估，得到了一个市场情况水平。在研究一中，我们集中在一个顾客大都来源于广泛产业的B2B服务供应商。在研究二中，我们则集中在对各行各业的服务有体验的个人消费者。这两个研究的结果都强烈的证明了隐蔽的满意度强度在将满意度转化成忠诚度的过程中起到了一个中心的作用。一个关键的发现并没有被隐藏，在这个报告中有指出，当满意度的判断被强烈持有时，满意度很大程度上能转化成忠诚度，同理，当满意度持有较为微弱时这种转化率就会减低，大概只有60%。外文翻译之二Customer loyalty in e-commerce: an exploration of its antecedents and consequences作者：Srini S. Srinivasana/ Rolph Andersona,/Kishore Ponnavolub国籍：American出处：Journal of Retailing ,2002(78),41-50原文正文：IntroductionAccording to the U.S. Census Bureaus Monthly Retail Trade Survey, Internet retail sales for 2000 were $25.8 billion, or 49% higher than 1999 sales of $17.3 billion.1 This rapid growth of e-retailing reflects the compelling advantages that it offers over conventional brick-and-mortar stores, including greater flexibility, enhanced market outreach, lower cost structures, faster transactions, broader product lines, greater convenience, and customization. However, e-retailing also comes with its own set of challenges. Competing businesses in the world of electronic commerce are only a few mouse clicks away. As a result, consumers are able to compare and contrast competing products and services with minimal expenditure of personal time or effort. According to Kuttner (1998, p. 20), “The Internet is a nearly perfect market because information is instantaneous and buyers can compare the offerings of sellers worldwide. The result is fierce price competition and vanishing brand loyalty.” Given the reduction in information asymmetries between sellers and buyers, there is a growing interest in understanding the bases of customer loyalty in online environments.Customer loyaltyEarly views of brand loyalty focused on repeat purchase behavior. For example, Brown (1952) classified loyalty into four categories, (1) Undivided loyalty, (2) Divided loyalty (3) Unstable loyalty, and (4) No loyalty, based on thepurchase patterns of consumers. Lipstein (1959) and Kuehn(1962) measured loyalty by the probability of product re-purchase. Some researchers (e.g., Day, 1969; Jacoby & Chestnut, 1978) have suggested that a behavioral definition is insufficient because it does not distinguish between true loyalty and spurious loyalty that may result, for example,from a lack of available alternatives for the consumer. In response to these criticisms, researchers have proposed measuring loyalty by means of an attitudinal dimension in addition to a behavioral dimension. Engel & Blackwell (1982) defined brand loyalty as “the preferential, attitudinal and behavioral response toward one or more brands in a product category expressed over a period of time by a consumer.” Jacoby (1971) expressed the view that loyalty is a biased behavioral purchase process that results from a psychological process. According to Assael (1992, p. 87), brand loyalty is “a favorable attitude toward a brand resulting in consistent purchase of the brand over time.” For our purpose, we define e-loyalty as a customers favorable attitude toward the e-retailer that results in repeat buying behaviorThe antecedents of e-loyaltyTo obtain a detailed perspective on the antecedents of e-loyalty, we first conducted interviews with forty-two individualsfifteen customers who purchased online, fifteen executives in the e-commerce arena, and twelve professional e-commerce website designers. Each of the individuals was asked six general questions about online shopping behavior. Additional probing questions were asked depending on the responses obtained. Most interviews lasted between ninety minutes and two hours. Based on these in-depth interviews we identified eight e-business factors that appeared to impact e-loyalty: (1) customization, (2) contact interactivity, (3) cultivation, (4) care, (5) community, (6) choice, (7) convenience, and (8) character. In summary, we hypothesize that: The greater the (1) level of customization, (2) contact interactivity, (3) customer cultivation, (4) care, (5) community, (6)
