消费者的决策过程和在线购买行为【外文翻译】

《消费者的决策过程和在线购买行为【外文翻译】》由会员分享，可在线阅读，更多相关《消费者的决策过程和在线购买行为【外文翻译】（12页珍藏版）》请在装配图网上搜索。

1、标题： Consumers Decision-Making Process and Their Online Shopping Behavior原文：IntroductionAmong all possible advantages offered by electronic commerce to retailers, the capacity to offer consumers a flexible and personalized relationship is probably one of the most important (Wind & Rangaswamy, 2001).

2、Online personalization offers retailers two major benefits. It allows them to provide accurate and timely information to customers which, in turn, often generates additional sales (Postma &Brokke, 2002). Personalization has also been shown to increase the level of loyalty consumers hold toward a ret

3、ailer (Cyber Dialogue, 2001; Srinivasan, Anderson, & Ponnavolu,2002). While there are several ways to personalize an online relationship, the capacity for an online retailer to make recommendations is certainly among the most promising (The e-tailing Group, 2003). Online, recommendation sources rang

4、e from traditional sources such as other consumers(e.g.,testimonies of customers on retail websites such as A) to personalized recommendations provided by recommender systems (West et al., 1999). To date, no study has specifically investigated and compared the relative influence of these online reco

5、mmendation sources on consumers product choices. Therefore, the main objective of this study is to investigate the influence of online product recommendations on consumers online product choices. In addition, we explore the moderating influence of variables related to recommendation sources and the

6、purchase decision.Literature reviewResearch on the use and influence of recommendations on consumers has typically been subsumed under personal influence or word-of-mouth (WOM) research. In addition, as noted by Rosen and Olshavsky (1987), research on opinion leadership and reference groups also rel

7、ates to the study of ecommendations and to influence in general. Recommendation sources are considered primarily as information sources. Andreasen (1968) proposes the following typology of information sources: (1) Impersonal Advocate (e.g., mass media), (2) Impersonal Independent (e.g., Consumer Rep

8、orts), (3) Personal Advocate (e.g.,sales clerks), and (4) Personal Independent (e.g., friends). Although research on personal influence and WOM focuses on the latter two information sources, it is note worthy that impersonal independent information sources such as Consumer Reports can also serve as

9、recommendation sources. Moreover, the Internet can provide consumers with an additional type of impersonal information source. For instance, electronic decision-making aids such as recommender systems are impersonal information sources that provide personalized information to consumers (Ansari,Esseg

10、aier,&Kohli, 2000). In an effort to extend Andreasens (1968) typology to computer-mediated environments, we assert that information sources can be sorted into one of four groups: (1) Personal source providing personalized information (e.g., “My sister says that this product is best for me.”); (2) Pe

11、rsonal source providing non-personalized information (e.g., “A renowned expert says that this product is the best.”); (3) Impersonal source providing personalized information (e.g., “Based on my profile, the recommender system suggests this product.”); (4) Impersonal source providing non-personalize

12、d information (e.g., “According to Consumer Reports, this is the best product on the market.”).In consumer research, studies on personal influence, social influence, or WOM, can be categorized as studies investigating personal sources providing personalized or non-personalized information. Furthermo

13、re, studies dealing with reference groups encompass such sources as well as impersonal sources that provide non-personalized information. Thus, a new area has emerged in consumer research, arising mainly from information technologies such as the Internet: that of impersonal sources that provide pers

14、onalized information (Alba et al., 1997; Ansari et al., 2000;Hubl & Trifts, 2000; Maes, 1999; Urban, Sultan, & Qualls,1999; West et al.,1999).Research on information sources suggests that personal and impersonal information sources influence consumers decision-making (Ardnt, 1967; Duhan et al., 1997

15、; Gilly et al., 1998; Olshavsky &Granbois, 1979; Price & Feick,1984). For instance, Price and Feick (1984) found that consumers planned to use the following information sources for their next durable good purchase: (1) Friends, relatives, and acquaintances, (2) Salespeople, (3) Publications such as

16、Consumer Reports. However, if much is known about the relative likelihood of consumers to consider recommendations in the course of their decision making process, little is known about how recommendations, especially in a computer-mediated environment, impact consumers product choices.Determinants o

17、f recommendation influenceThe current study focuses on three determinants that could influence the impact of computer-mediated recommendations on consumers online product choices: the nature of the product recommended, the nature of the website on which the recommendation is proposed, and the type o

18、f recommendation source. Prior research has shown that the type of product affects consumers use of personal information sources and their influence on consumers choices (Bearden & Etzel, 1982; Childers & Rao, 1992;King & Balasubramanian, 1994). Nelson (1970) suggests that goods can be classified as

19、 possessing. either search or experience qualities. Search qualities are those that “the consumer can determine by inspection prior to purchase,” and experience qualities are those that “are not determined prior to purchase” (Nelson, 1974,p. 730). Since it is difficult or even impossible to evaluate

20、 experience products before purchase, consumers should rely more on product recommendations for these products than for search products. In support of this view, King and Balasubramanian (1994) found that consumers assessing a search product (e.g., a 35-mm camera) are more likely to use own-based de

21、cision-making processes than consumers assessing an experience product, and that consumers evaluating an experience product (e.g., a film-processing service) rely more on other-based and hybrid decision-making processes than consumers assessing a search product. The nature of the website can also in

22、fluence the impact of a given recommendation. Based on previous website classifications (Hoffman, Novak, & Chatterjee, 1995; Spiller & Lohse, 1998), Senecal and Nantel (2002) suggestthat recommendation sources can be used and promoted by three different types of websites: sellers (e.g., retailer or

23、manufacturer websites such as A), commercially linked third parties (e.g., comparison shopping websites such as MyS), and non-commercially linked third parties (e.g., product or merchant assessment websites such as Consumerreports.org). More independent websites such as non-commercially linked third

24、 parties that facilitate consumers external search effort by decreasing search costs are assumed to be preferred by consumers (Alba et al., 1997; Bakos, 1997; Lynch & Ariely, 2000). By providing more alternatives to choose from and more objective information, independent websites should be perceived

25、 as more useful by consumers. In addition, prior research on attribution theory suggests that consumers discredit recommendations from endorsers if they suspect that the latter have incentives to recommend a product (for reviews, refer to Folkes, 1988; Mizerski, Golden, & Kernan, 1979). According to

26、 the discounting principle of the attribution theory (Kelley, 1973), which suggests that a communicator will be perceived as biased if the recipient can infer that the message can be attributed to personal or situational causes, consumers would attribute more non-product related motivations (e.g., c

27、ommissions on sales) to recommendation sources that are promoted by commercially linked third parties and sellers than independent third party websites. Consequently consumers would follow product recommendations in a greater proportion when shopping on more independent than on less independent webs

28、ites. In light of research on consumers use of relevant others in their pre-purchase external search efforts (Olshavsky &Granbois, 1979; Price & Feick, 1984; Rosen & Olshavsky,1987) and in consideration of the emergence of online information sources providing personalized recommendations (Ansari et

29、al., 2000), Senecal and Nantel (2002) assert that online recommendation sources can be sorted into three broad categories: (1) other consumers (e.g., relatives, friends and acquaintances), (2) human experts (e.g., salespersons, independent experts), and (3) expert systems such as recommender systems

30、. We posit that these online recommendation sources will have different levels of influence on consumers online product selection. Brown and Reingen (1987) suggest that information received from sources that have some personal knowledge about the consumer have more influence on the latter than sourc

31、es that have no personal knowledge about the consumer. Thus, a recommendation source providing personalized information to consumers (e.g., recommender system) should be more influential than a recommendation source providing non-personalized information (e.g., other consumers). The fact that both f

32、actors, the origin (source) of a recommendation as well as the type of website on which it is made, have an impact on the likelihood it has to be followed may find its explanation in Kelmans (1961) work on source credibility. Kelman (1961) suggests that credibility is a product of expertise and trus

33、tworthiness. Expertise can be viewed as the perceived ability of an information source to know the right answer and trustworthiness as the perceived information sources motivation to communicate this expertise without bias (McGuire, 1969). Although moderated by contextual factors (for a review, refe

34、r to Sternthal, Phillips, &Dholakia, 1978), source expertise and trustworthiness have been found to be positively correlated with consumers attitude toward the brand, and consumers behavioral intentions and behaviors (Gilly et al., 1998; Harmon & Coney, 1982;Lascu, Bearden, & Rose, 1995; Tybout, 197

35、8). HypothesesBased on the preceding review of the literature we postulate that personal information sources as well as impersonal information sources providing product recommendations (Price & Feick, 1984) will influence consumers in computer-mediated environments such as the Internet and the World

36、 Wide Web. We thus formulate the following general hypothesis.H1. Consumers who consult an online information source recommending a given brand will select that brand in a greater proportion than consumers who do not consult an online recommendation source.As for the impact that such a recommendatio

37、n will have on consumers choice, we formulate three additional hypotheses. First, we posit that the nature of the product for which a recommendation is provided will influence the likelihood that it will be followed. Based on prior research on the relationship between product type and personal infor

38、mation source influence (Bearden & Etzel, 1982; Childers & Rao,1992; King & Balasubramanian, 1994), we put forward the following hypothesis.H2. Online recommendations for experience products will be followed in a greater proportion than online recommendations for search products. Second, based on Al

39、ba et al. (1997), Bakos (1997) and Lynch and Ariely (2000), we propose that online product recommendations from more independent websites are more influential than those from less independent websites. We therefore put forth the following hypothesis.H3. Online product recommendations consulted on “n

40、on-commercially linked third party” websites will be followed in a greater proportion by consumer than if consulted on “commercially linked third party” websites, and online product recommendation consulted on the latter type of websites will be followed in a greater proportion than if consulted on

41、“seller” websites. Finally, we believe, based on the literature which has dealt with the issue of consumers use of relevant others in their pre-purchase external search efforts, that personalized recommendations will have a greater influence on consumers than non-personalized ones (Brown & Reingen,

42、1987). Thus follows hypotheses four.H4. Recommendations from information sources offering personalized recommendations (e.g., recommender system) will be followed in a greater proportion by consumers than recommendations from information sources providing non-personalized recommendations.In addition

43、 to this set of hypotheses, which pertains to the variables that moderate the influence of an online recommendation, we formulate a set of three hypotheses which consider potential reasons for which various online recommendation sources may differ in their influence on consumers choices.First, we ex

44、pect that the recommendation source “other consumers” will be perceived as less expert than “human experts” and “recommender systems”. However, based on the discounting principle of attribution theory (Kelley, 1967), the recommendation source “other consumers” should be perceived as more trustworthy

45、 than human experts and recommender systems since the latter two recommendation sources are more susceptible to non-product related attributions. Second, since consumers may also attribute non-product related motivations more easily to recommendation sources promoted by websites that are not clearly

46、 independent, we predict that the type of website will have an impact on the perception of the recommendation sources trustworthiness. For instance, a human expert who recommends a product on a seller website may be perceived by consumers as less trustworthy than if that person recommended the same

47、product on an independent third party website. Thus, the following hypotheses are posited.H5a. The online recommendation sources “human experts” and “recommender system” will be perceived as possessing more expertise than the online recommendation source “other consumers.”H5b. The online recommendat

48、ion sources “human experts” and “recommender system” will be perceived as less trustworthy than the online recommendation source “other consumers.”出处：Seneal S，JNante1 influence of online product recommendations on consumersonline choicesJJournal of Retail1ing ，2004，80标题：消费者的决策过程和在线购买行为译文：摘要:由电子商务向零售

49、商提供的所有可能的优势中，有能力向消费者提供灵活和个性化的关系可能是最重要的(Wind & Rangaswamy, 2001)。在线个性化为零售商提供两大好处。这使他们能向客户提供准确和及时地信息，反过来，经常产生额外的销量(Postma &Brokke, 2002)。个性化也被证明能提高消费者对零售商的忠诚度水平(Cyber Dialogue, 2001; Srinivasan, Anderson, & Ponnavolu,2002)。虽然存在着多种发展个性化关系的方法，然而对于网上零售商提供建议无疑是最有前途的(The e-tailing Group, 2003)。在线建议范围从传统的来源

50、，如推荐系统为消费者（例如，在亚马逊等零售商网站提供证词的顾客）所提供给的个性化建议。 (West et al., 1999)迄今为止，还没有研究专门调查并比较这些在线推荐来源对消费者产品选择的相对影响。因此，本研究的主要目的是探讨网上推荐产品对消费者在线产品选择的影响。此外，我们还探讨了有关建议的来源与购买决策变量的干扰影响。文献：推荐对消费者的作用和影响研究通常归结为个人的影响力或口碑的研究。此外，Rosen 和Olshavsky 在1987年指出，领导和相关群体观点的研究大体上也与在线推荐的课题和影响有关。推荐来源，被认为是首要的信息来源。Andreasen (1968)提出以下几种信息

51、来源：（1）非个人的提倡（例如，大众媒介），（2）客观独立的（例如，消费者报告），（3）人员建议（例如，销售员），（4）个人独立的（例如，朋友）。虽然对个人影响力和口碑研究的重点是后两种信息源，值得注意的是，如消费者报告的独立信息源也可以作为建议的来源。此外，互联网可以向消费者提供额外类型的的客观信息来源，比如，为消费者提供个性化信息的推荐人系统这样的电子决策帮助(Ansari,Essegaier,&Kohli, 2000)。在一个延伸Andreasen (1968)类型学理论对以计算机作为媒介的环境中，我们主张信息资源可分为四大类中的一种：（1）提供个性化信息的个人资料来源（比如，“我妹妹说

52、，本产品对我是最适合的。”）；（2）提供非个性化信息的个人资料来源(例如，“一位著名的专家说,本产品是最好的” ）；（3）提供个性化的客观源(例如,根据我的推测，推荐系统表明这种产品。”）；（4）提供非个性化信息的客观源（例如，“根据消费者报告，这是市场上最好的产品。”）。在消费者调查中，对个人影响、社会影响以及口碑的研究，可以归类为提供个性化和非个性化的个人信息来源的研究。此外，研究处理涵盖这些信息来源的相关组以及提供非个性化信息的客观来源。因此，消费者研究的一个新领域出现了，它主要从信息技术，比如互联网提供个性化信息的客观来源中产生(Alba et al., 1997; Ansari et

53、 al., 2000;Hubl & Trifts, 2000; Maes, 1999; Urban, Sultan, & Qualls,1999; West et al.,1999)。信息来源的研究表明：个人和客观信息资源影响消费者决策(Ardnt, 1967; Duhan et al., 1997; Gilly et al., 1998; Olshavsky &Granbois, 1979; Price & Feick,1984)。举例来说，Price and Feick (1984)发现，消费者计划利用以下信息来源服务于将来的耐用品购买：（1）朋友、亲戚、相识，（2）销售人员，（3）消费者

54、报告等刊物。然而，如果在消费者决策过程中，消费者考虑相对可以被更多了解的推荐的话，那么推荐对消费者产品选择的影响，尤其是在电脑媒介的环境中，将会被更少的了解到。 影响推荐的因素目前的研究主要集中在以计算机为媒介的可能会影响消费者在线产品选择的三个推荐因素:：产品的性质的建议，推荐网站的性质，推荐来源的类型。之前的研究表明产品的类型会影响消费者对个人信息资源的使用以及其影响对消费者的选择(Bearden & Etzel, 1982; Childers & Rao, 1992;King & Balasubramanian, 1994).。Nelson (1970)认为商品可以看作是拥有。由于它很难

55、甚至无法评估购买前体验的产品，消费者应该更多地依靠这些产品的产品推荐而不是搜索产品。为了支持这一观点，King and Balasubramanian (1994)发现，消费者评估一个搜索的产品（比如，一个35毫米相机）时，更有可能根据自己的决策过程而不是其他消费者对体验的产品的评价，而消费者评价一个体验产品（如电影处理服务）则更多地依靠其他和混合型的决策过程而不是消费者对一件搜索的产品的评估。网站的性质也会影响到一个既定建议的效果。在(Hoffman, Novak, & Chatterjee, 1995; Spiller & Lohse, 1998)以往的网站分类的基础上，Senecal a

56、nd Nantel (2002)提出推荐来源可以通过三种不同的网站类型进行使用和推广，卖方（例如，零售商或者制造商网站如亚马逊），商业联系第三方（例如,，MyS一类型的比较网站）和非商业联系第三方（例如,，Consumerreports.org这样的产品或商家的评估网站等）。更多的比如连接第三方团体的独立网站，通过降低消费者的搜寻成本的外部努力，被假定为消费者的首选(Alba et al., 1997; Bakos, 1997; Lynch & Ariely, 2000)。通过提供更多的以供选择的方案和更客观的信息，独立网站应被消费者认为更加有用。此外，先前的归因理论表明，如果消费者怀疑代言人

57、有诱因才去推荐产品，则不会相信他们的推荐(for reviews, refer to Folkes, 1988; Mizerski, Golden, & Kernan, 1979)。根据贴现(Kelley, 1973)归因理论的原则，如果接收人可推测该消息归因为个人或情景原因，则沟通者是被视为偏颇的，消费者会将更多非产品相关的动机归看作是推荐来源，通过商业联系第三方团体和卖家而不是独立第三方网站来促进产品的销售。因此，消费者在更加独立而不是很少独立的网站上购物时，很大程度上将会遵循产品的推荐。根据(Olshavsky &Granbois, 1979; Price & Feick, 1984;

58、Rosen & Olshavsky,1987)消费者在购买前的外部搜寻努力中，使用相关他人信息的研究，并考虑网上信息资源的出现提供了个性化的建议(Ansari et al., 2000)，Senecal and Nantel (2002)断言，网上推荐的来源可分为三大类：（1）其他消费者（例如,亲戚,朋友和熟人）；（2）人类专家（例如，销售人员,独立专家）；（3）如推荐人系统的专家系统。我们假设，这些网上推荐资源会对消费者在线产品选择产生不同程度的影响。信息从一些对消费者有个人见解的资源接收作用要落后于对消费者没有个人见解的资源。因此，推荐资源为消费者提供个性化的信息，比如推荐系统比没有个性化

59、信息的推荐资源（例如其他消费者）更具影响力。事实上，一个推荐的起源和其网站的性质，这两者都有影响，在接下来Kelmans (1961)对资源可信度的工作中会有解析。Kelman (1961)表明，诚信是一种专业知识和信任的产品，专业知识可以看作是一个知道正确答案的信息资源的能力，信任可看作是不带偏见的专业知识沟通这样的信息资源动机。尽管有环境因素的调节(for a review, refer to Sternthal, Phillips, &Dholakia, 1978)，源的专业知识和诚信被发现与消费者对品牌、消费者行为的意图和行为密切相关(Gilly et al., 1998; Harmo

60、n & Coney, 1982; Lascu, Bearden, & Rose, 1995; Tybout, 1978)。假设基于前面的文献回顾，我们假设，个人信息资源以及提供产品推荐的客观的信息源会作用于以电脑为媒介的环境，比如因特网和万维网。因此，我们制定了一下的大体的假设。H1：在线咨询信息资源的消费者在一定程度上比不在线咨询推荐信息的消费者，更会选择那个推荐的给定的品牌。至于这样的建议会对消费者选择的影响，我们假定另外三种假说。第一，我们假定提供推荐的产品的性质会影响到后面的可能性。根据以往对产品类型和个人信息资源的关系的研究(Bearden & Etzel, 1982; Childe

61、rs & Rao,1992; King & Balasubramanian, 1994)，我们提出如下假设。H2:经验产品的在线推荐在一定程度上比搜索产品的在线推荐效果更好。第二，根据Alba et al. (1997), Bakos (1997) and Lynch and Ariely (2000),我们认为在独立网站的在线产品推荐比那些较少独立的网站影响力更大。因此我们提出了以下假说。H3提供在线产品推荐的非商业联系的第三方网站比商业联系的网站在一定程度上将受到消费者的追随，后一种类型的网站所提供的在线产品推荐比卖家网站在一定程度上会被消费者跟随。最后，基于解决消费者在购买前外部搜寻努力

62、中对相关者使用的研究，我们认为，个性化的推荐比非个性的推荐将会对消费者产生更大的影响(Brown & Reingen, 1987).因此，我们提出了假设四。H4：提供个性化推荐的信息源（例如推荐系统）的推荐比提供非个性化信息源的推荐，在一定程度上会被消费者所跟随。此外，这个假说的设置涉及到影响在线推荐效果的变量，因此，我们制定了三个假设以考虑不同在线推荐源对消费者选择产生不同影响的潜在原因。首先，我们预计该推荐源的其他消费者将被视为比“专家“和“推荐系统”的期望更低。然而，基于(Kelley, 1967归因理论贴现的原则，推荐源“其他消费者”被认为比“专家”和“推荐系统”更值得信赖。因为后两者推荐源比非产品相关属性更易受到影响。其次，消费者也容易受到没有明确独立提出非产品相关动机的推荐源的影响，我们预测网站的类型将会对此推荐源可信度的感知产生影响。例如，一个在卖家网站推荐产品的专家如果也在一个独立第三方网站推荐相同的产品，那么他可能会被消费者视为缺乏可信度。因此，以下的推测是假设的。H5a：在线推荐源“专家”和“推荐系统”被视为比“其他消费者”拥有更多的专业知识。H5b.：在线推荐源“专家”和“推荐系统”的信任度被视为低于“其他消费者”。