Differential Effects of Provider Recommendations and Consumer Reviews in E-Commerce Transactions. an...

Journal of Management Information Systems / Summer 2012, Vol. 29, No. 1, pp. 237–272.

© 2012 M.E. Sharpe, Inc. All rights reserved. Permissions: www.copyright.com

ISSN 0742-1222 (print)/ISSN 1557-928X (online)

DOI: 10.2753/MIS0742-1222290107

Differential Effects of Provider Recommendations and Consumer Reviews in E‑Commerce Transactions: An Experimental Study

AlEXANDEr BENlIAN, ryAD TITAh, AND ThOMAS hESS

AlexAnder BenliAn is a chair professor of information systems, especially electronic services, at TU Darmstadt, Germany. he holds a Ph.D. in business administration and management information systems from the University of Munich. his research interests include IT services, evaluation and selection of enterprise application systems, and rec-ommender systems in online commerce. his work has been published in international journals such as Journal of Management Information Systems, Information Systems Journal, European Journal of Information Systems, International Journal of Elec-tronic Commerce, Information & Management, Decision Support Systems, Business & Information Systems Engineering and presented at conferences such as International Conference on Information Systems (ICIS), European Conference on Information Systems (ECIS), and Americas Conference on Information Systems (AMCIS).

ryAd TiTAh is an assistant professor of information systems at hEC Montreal. Prior to joining hEC Montreal, he was an associate professor of information systems at EMlyON Business School. his main research interests are in information technol-ogy acceptance, use, and impact in both public and private organizations. his work has been published in journals such as Information Systems Research, Information Technology and People, International Journal of Electronic Government Research, and MIS Quarterly. his research has won several prestigious awards, including a Best IS Publication of the year Award granted by ICIS 2007 and its Senior Scholars, a highly Commended Paper Award, granted by Emerald literati Network Awards of Excellence, 2007, and the ACM SIGMIS Best Dissertation Award 2011.

ThomAs hess is a professor of management information systems at the University of Munich, Germany, where he also serves as director of the Institute for Information Systems and New Media and as coordinator of the Center for Internet research and Media Integration. he holds a Ph.D. in management from the University of St. Gallen, Switzerland. his research interests include digitalization strategies, digital products, media management, and economics of information systems. his work has appeared in international journals such as Journal of Management Information Systems, Communi-cations of the ACM, Information Systems Journal, International Journal of Electronic Commerce, Electronic Markets, Information & Management, and Information Society. he has also published in the proceedings of conferences such as International Confer-ence on Information Systems (ICIS), European Conference on Information Systems (ECIS), and Americas Conference on Information Systems (AMCIS).

238 BENlIAN, TITAh, AND hESS

ABsTrAcT: Despite the importance of online product recommendations (OPrs) in e-commerce transactions, there is still little understanding about how different recommendation sources affect consumers’ beliefs and behavior, and whether these effects are additive, complementary, or rivals for different types of products. This study investigates the differential effects of provider recommendations (Prs) and consumer reviews (Crs) on the instrumental, affective, and trusting dimensions of consumer beliefs and shows how these beliefs ultimately influence continued OPr usage and product purchase intentions. This study tests a conceptual model linking Prs and Crs to four consumer beliefs (perceived usefulness, perceived ease of use, perceived affective quality, and trust) in two different product settings (search prod-ucts versus experience products). results of an experimental study show that users of Prs express significantly higher perceived usefulness and perceived ease of use than users of Crs, while users of Crs express higher trusting beliefs and perceived affective quality than users of Prs, resulting in different effect mechanisms toward OPr reuse and purchase intentions in e-commerce transactions. Further, Crs were found to elicit higher perceived usefulness, trusting beliefs, and perceived affective quality for experience goods, while Prs were found to unfold higher effects on all of these variables for search goods.

Key words And phrAses: consumer reviews, e-commerce, online product recommenda-tions, perceived affective quality, perceived usefulness, provider recommendations, technology acceptance and usage, trusting beliefs.

As opposed To offline reTAil chAnnels, e-commerce consumers cannot try out prod-ucts before making purchases, which significantly increases their level of uncertainty regarding the quality of the products, and thus hinders their purchasing decisions. To compensate for the absence of quality inspections in online markets, many e-commerce vendors provide system-filtered recommendations (hereafter called “provider recom-mendations” [Prs]) that recommend products to consumers based on their past buying behavior or on the preferences of other like-minded consumers. however, product rec-ommendations may also stem from reviews written by consumers about the quality of products based on personal experiences with the products (hereafter called “consumer reviews” [Crs]). Due to their high level of acceptance among consumers, different types of information technology (IT)–enabled Prs and Crs—defined in this paper as different types of online product recommendations (OPrs)—are becoming increas-ingly available on Web sites to provide customers with shopping assistance, improve their decision quality, and help buyers and sellers reduce information overload [69]. It is noteworthy that the role of OPrs is important for both consumers and suppliers, as they represent a critical encounter tool for value co-creation [109]. It is estimated that at least 43 percent of e-commerce Web sites already offer Prs and Crs [36]. however, and although the number of different forms of OPrs on e-commerce Web sites has exploded in recent years, there is still confusion about Prs’ and Crs’ isolated effectiveness and about their differential effects on users’ beliefs and behavior. OPrs that allow customers to evaluate products and services are thought to significantly

rECOMMENDATIONS VErSUS rEVIEWS IN E-COMMErCE TrANSACTIONS 239

affect customers’ decision making and behavior [16, 42]. As such, understanding the decision process and particularly the effect of consumers’ perceptions and beliefs on OPr usage intentions and product purchasing behavior becomes critical to the success of e-commerce platforms. The present paper aims at answering the following questions: Are consumers more responsive to recommendations generated by the e-commerce provider through sophisticated agent technology or are they more inclined to follow recommendations generated by other consumers (“human agents”)? More specifically, how do the two sources of recommendations (i.e., Prs and Crs) compare in evok-ing consumers’ instrumental, affective, and trusting beliefs and ultimately affecting their subsequent OPr usage continuance and product purchase intentions? Are they complements or substitutes in their respective effect mechanisms? Although the study of the effects of OPrs on individual product choice or other outcome criteria is not a new field of research [26, 51, 55, 57, 76, 87, 104], there has been little emphasis on unraveling the differential instrumental, affective, and trusting effects of Prs and Crs on e-commerce Web sites for different product types and assessing how these ef-fects translate into OPr reuse and product purchase intentions. Addressing this gap is conceptually useful because it reexamines accepted relationships [102] between single OPr effects on different sets of beliefs and behaviors and provides a finer-grained knowledge about the different mechanisms affecting OPr reuse initiated by different information sources. It also proposes a more holistic and integrative understanding of the effect range of different OPrs, which has been overlooked in previous OPr literature. however, it may also provide e-commerce vendors with actionable guidelines regarding the positioning and salience of OPrs for different product types at different stages of a consumer’s buying process.

By investigating the effects of two different recommendation sources in an extended technology acceptance model (TAM), including instrumental, affective, and trusting dimensions of consumer beliefs, the present study extends previous research literature related to the effects of OPr on consumer beliefs and behavior in three important ways. First, it exposes and dissects the distinct effects of Prs and Crs by examining their influence on three core belief categories: (1) instrumental beliefs (i.e., perceived usefulness and ease of use), (2) affective beliefs (i.e., perceived affective quality), and (3) trusting beliefs. It also unravels the complementary effect of Prs and Crs on the core user beliefs. Second, it examines how these three belief categories mediate the effect of Prs and Crs on OPr reuse and product purchase intentions based on OPr use. Third, it investigates the moderating role of product type (i.e., search versus experience goods) on Prs and Crs, which has been neglected in previous OPr studies.

recommendation Sources: Provider recommendations Versus Consumer reviews

informATion sources cAn generAlly Be sorTed inTo one of four groups [87]: (1) per-sonal source providing personalized information (e.g., “My sister says that this product is best for me”); (2) personal source providing nonpersonalized information (e.g., “Other consumers report about their experiences with this product”); (3) impersonal


source providing personalized information (e.g., “Based on my profile or the profile of my affinity group, the e-commerce provider’s recommender system suggests this product”); and (4) impersonal source providing nonpersonalized information (e.g., “According to consumer reports, this is the best product on the market”). Although many different recommendation types relying on these various information sources exist on e-commerce Web sites, this study focuses on two specific recommendation sources corresponding to information sources (2) (i.e., Crs) and (3) (i.e., Prs), which are widely deployed on e-commerce Web sites. As shown in Table 1, Prs and Crs exhibit different characteristics that are argued in the present paper to have different effects on individual beliefs and intentions in e-commerce transactions.

Prs are Internet-based software that carry out a set of operations on behalf of users and provide shopping advice based on users’ needs, preferences, profiles, and previ-ous shopping activities [59]. They have been proposed as support tools for consumers at various stages of their decision-making process. Different types of Prs have been developed and are currently used within e-commerce Web sites. Content-based and collaborative-filtering-based recommendations are the most widely used classes of Prs [101]. Content-based filtering recommendations are typically based on a set of algorithms that derive recommendations for a particular user from that user’s profile or from knowledge about that user’s past behavior [7]. A user profile is based on ex-plicit interests and on past behavior of the user. For example, a content-based filtering system would recommend a book to a user based on the user’s expressed interests

Table 1. Characteristics of Provider recommendations and Consumer reviews

CharacteristicProvider recommendations

(Pr)Consumer reviews

(Cr)

Author/creator of content Provider Other consumers/usersOriginality of content System-filtered content

extracted from statistical analyses; complemented with product item descriptions and key attributes

Original, firsthand content

Source of recommendation preferences

Attribute-based preferences based on past consumer behavior and profiles

Preferences based on past consumer experience or opinions

Number of data points included in the recommendation

Many (very large data sets) Few

Media richness of recommendations

Text, pictures (multimedia) (Predominantly) text based

Level of e-commerce provider’s control over content layout

High Low


about books in his or her profile or based on the user’s previous book purchase his-tory. Alternatively, collaborative-filtering recommendations mimic word-of-mouth recommendations and use the buying behavior of like-minded people to generate recommendations [9]. recommendations are commonly extracted from statistical analysis of patterns and analogies of data drawn from evaluations of items (ratings) given by other users or implicitly by monitoring the behavior of other users in the system [62]. For example, a collaborative-filtering-based Pr would recommend a book to a consumer because other consumers within the same affinity group (i.e., a group of consumers with similar preferences) purchased the book or rated it highly. Prs usually include common product descriptions (e.g., brief description of the contents of a book or album) and the provision of key product attributes (e.g., price, label, or overall length of a music album).

recommendations that are based on user-created digital content such as consumer reviews are not based on system-filtered content but, rather, on original, first-hand content, where a software system does not interfere with the recommendation gen-eration process (see Table 1). Thus, Crs are not generated by ITs; instead, they are mediated by them [109]. Furthermore, Crs draw their data points from usage experi-ences and opinions that are directly reported by other consumers [21], whereas Prs automatically and statistically process past buying behaviors or interest profiles in addition to providing key product attributes and descriptions. Moreover, because Crs cannot be presented in a standardized and consistent layout across consumers, Web site providers have less control over the structure of presentation format of Crs than they do over Prs. While consumer reviews are most often based on text and appear with different text length and numbers of paragraphs, Prs are always presented in a consistent layout as designed by the Web site provider, including text, pictures, and sometimes multimedia files (i.e., audio or video). Finally, Crs are based on few data points (e.g., experiences, opinions) from a low number of fellow consumers, whereas Prs aggregate and evaluate many data points stemming from a multitude of fellow consumers within a particular affinity group.

In general, Prs are perceived as the provision of more or less personalized product items to consumers. They usually include the presentation of provider-generated product descriptions and key product attributes. Crs are sometimes also used to recommend different product items (e.g., a fellow consumer suggests better prod-uct alternatives in a review), but their main focus is to provide feedback on a given product item (e.g., recommendations on use of presented product item). Both types of recommendation sources are usually provided in conjunction with each other on e-commerce platforms.

Although a few studies (e.g., [30, 57, 89]) have analyzed the effect of both recom-mendation types (i.e., Prs and Crs) on a set of dependent variables, they did not specifically focus on how a comprehensive set of instrumental, affective, and trusting beliefs elicited by Prs and Crs affected continued usage of OPrs and purchase inten-tions. Further, these studies did not examine how the effect mechanisms of different recommendation sources play out in different product contexts, which is another important focus of the present paper.


research Model and hypotheses

This sTudy’s reseArch model drAws on cogniTive psychology, affective psychology, as well as on recommendation agent, acceptance, and trust literatures. As shown in Figure 1, our model is TAM-related and includes affective and trusting beliefs in ad-dition to TAM instrumental beliefs to explain individual intentions. The instrumental, affective, and trusting beliefs and their relationships with OPr types are based on the classification of user evaluations of OPrs proposed by Xiao and Benbasat [104], which models the influence of OPrs on trust (i.e., trusting beliefs), perceived usefulness, perceived ease of use (i.e., instrumental beliefs), and satisfaction (i.e., affective beliefs). These three dimensions could be linked to the concept of “motivational affordance” proposed by Zhang [106], who argues that the different motivational sources of users should be taken into account when designing information systems (IS). In effect, users tend to use and continue to use IS to fulfill various psychological, cognitive, social, and emotional needs. hence, the properties of an object (or technology) that support these motivational needs (i.e., the object’s “motivational affordance” [106, p. 45]) can influence whether, how, and how much the object (or technology) will be used.

Perceived affective quality (i.e., affective belief) was included in the conceptual model because it captures an individual’s primitive and multifaceted affective reac-tions to OPrs. As opposed to perceived enjoyment or satisfaction, which are also considered affective reactions to IT, but which are situated at a secondary or higher level than perceived affective quality [108], we argue that perceived affective quality allows capturing a more foundational dimension of affective consumer reactions. Further, trusting beliefs were included in the model because past research has shown that users had difficulty in determining whether human and software-based OPrs are

Figure 1. research Model


capable of product screening and evaluation or whether such OPrs act in the interest of the users or are manipulated by the e-commerce provider [104]. For example, in a focus group experiment, Andersen et al. [6] found that trust in OPrs was the most important expectation users had and that it represented a crucial aspect of a consumer’s interaction with an OPr. As such, users’ trusting beliefs encompass important social and interactional dimensions that ought to be considered when comparing different kinds of user evaluations.

The Effects of Prs and Crs on Consumers’ Instrumental, Affective, and Trusting Beliefs

In the context of e-commerce transactions, empirical research indicates that OPrs help consumers manage the overwhelming amount of information and choices available in electronic environments by guiding them to a set of more relevant products that are likely to fit their needs [42, 87]. So, OPrs enable consumers to cope with information overload by reducing their search costs, which also enhances their effectiveness in making satisfying buying decisions [38, 55]. OPrs are also perceived as being more than just technologies or tools. They represent virtual decision aids that help execute more effective decisions. The theory of human information processing [88] argues that, because of limitations in their cognitive capacity that include limited working memory and limited computational capabilities, people tend to “satisfice” when processing information and making decisions. This theory also posits that consumers reduce their cognitive burden when making decisions by adopting a two-stage decision-making process. In the first stage, the set of products (alternatives) is reduced to a manage-able level, while in the second stage, the reduced set of products is evaluated in detail. Both Prs and Crs were found to significantly affect perceived usefulness of the OPr because of their ability to reduce the cognitive burden of sifting through multiple alter-natives, which consequently helped better evaluate product items [42, 57]. however, and although the impact of Prs and Crs on perceived usefulness has been examined separately in previous research (e.g., [52, 76, 97] for Prs; [103] for Crs), the distinct effect mechanisms of both types of OPrs on perceived usefulness have not yet been explicitly contrasted. In this study, we argue that Prs are more effective in reducing search costs for consumers and have, as a consequence, a stronger impact on perceived usefulness than Crs. This argument is based on cognitive fit theory (CFT) [95], which was initially proposed to explain how matching problem representations (e.g., tables, graphs, and matrices) to different tasks can improve problem solving. CFT suggests that technology can be used to increase task performance if there is a good fit between the task and the information or problem representation. Such fit is argued to result in more efficiency and effectiveness, manifested as increased accuracy and speed in problem solving. For example, when the information format matches the task, users are able to search the information space more efficiently and have better information recall [46, 48], thus lowering the cognitive costs and increasing the benefits of the interface [50]. Therefore, in order to understand how the cognitive fit between the information representation in Prs and Crs and the consumer’s task of evaluating dif-


ferent types of recommendations affects OPr reuse and product purchase intentions, it is important to first understand how it directly affects users’ beliefs. Given the above, we expect that an OPr type that presents limited but decision-relevant attributes will be preferred by individuals in terms of perceived usefulness. Indeed, based on the distinguishing features between Pr and Cr depicted in Table 1, we believe that Pr users can immediately see and evaluate the product’s key attributes (e.g., short de-scription, key features) and their values (e.g., price). In contrast, Cr users must infer both what the most important products attributes are as well as the different values for each attribute, by scanning all information posted by other users. This process is called “intuitive regression” [61] and is more cumbersome and inefficient than using Prs. Following the same idea, Parboteeah et al. [68] found that task-relevant cues (i.e., all cues that enhance the utilitarian value) of a Web site have a stronger impact on perceived usefulness than mood-relevant cues (i.e., cues that create an atmosphere that has the potential to make the shopping experience more pleasurable). In the context of the present study, we argue that because they provide more task-relevant cues, Prs facilitate and enable the consumer’s buying decision better than Crs and will therefore be perceived by the consumer as being more useful [32]. As such,

Hypothesis 1: Consumers will perceive greater perceived usefulness of PRs than of CRs.

Moreover, as opposed to Prs, Crs most often include only textual comments and do not contain pictures that would make key product information more accessible [23]. Empirical studies in educational psychology (i.e., reading comprehension) found that the cognitive effort for reading full-text sentences and passages is higher as compared to screening pictures and small chunks of key product information [20]. Given that Crs1 consist of wordy text comments that differ in length and writing style, consumers have to first sift through this unstructured text to get to relevant product information that helps them in their shopping task. Taken together, these arguments lead to the proposition that Prs are perceived as easier to use in evaluating products and sup-porting the shopping task than Crs. Therefore, Prs should increase perceived ease of use more than Crs:

Hypothesis 2: Consumers will perceive greater perceived ease of use of PRs than of CRs.

IT artifacts (e.g., recommendation agents or e-mail systems) have been found to generate cognitive and affective arousal in IT users, thus showing both hedonic and utilitarian value of IS [28]. Drawing on consumer research and retailing literature [45], studies related to Internet shopping and e-commerce OPrs have constantly shown that various Web site characteristics enhance users’ perceptions of hedonic value. Ex-amples of such characteristics are socially rich text contents, personalized greetings, and pictures of humans [40, 76]. More generally, the effects of hedonic beliefs have also been identified as important determinants of online customer loyalty [56] and have been found to play at least an equal role as instrumental beliefs [94]. Although some studies have investigated the affective impact of OPrs in e-commerce focusing


on specific characteristics (e.g., anthropomorphic elements [76]), to the best of our knowledge, no study has yet explored the differential effects of Prs and Crs on the hedonic dimensions of OPrs.

In this study, perceived affective quality is used to capture the hedonic (i.e., affec-tive) value of consumers’ interaction with OPrs. According to Zhang and li [107], this construct refers to an individual’s perception of an object’s ability to change his or her core affect, and is a neurophysiological state that is consciously accessible as the simplest raw (nonreflective) feeling evident in moods and emotions and underlies simply feeling good or bad, drowsy or energized [83]. Core affect is defined as an in-tegral blend of hedonic or valence value (pleasure–displeasure, i.e., the extent to which one is generally feeling good or bad) and arousal or activation value (sleepy–activated, i.e., the extent to which one is feeling engaged or energized). It is considered to be free of any implied cognitive structures and at the heart of emotion, mood, and any other emotionally charged events [82]. The concept of core affect is similar to what some psychologists call “primary emotions” that precede higher-order emotional episodes such as anger, fear, or enjoyment [19]. In the context of IS research, perceived affective quality helps identify an individual’s fundamental and primitive affective reactions to an IT and has to be distinguished from secondary or higher-level reactions such as computer anxiety, perceived enjoyment, or satisfaction that capture specific feelings and emotional states [108].

In the context of this study, we argue that Crs are likely to produce higher perceived affective quality than Prs. Indeed, the stories and narratives of personal experiences including specific examples make up the bulk of what a reader will find in Crs [21]. As Deighton et al. [29] pointed out, stories have an ability to draw in and cause the reader to empathize with the feelings of the writer, in effect creating vicarious expe-rience. Moreover, communication research has shown that vivid, concrete examples have strong impact on users’ beliefs and affections [78, 92]. In particular, some studies in persuasion and reading research found that narrative messages are more concrete, persuasive, and emotional than statistical information [67, 84]. Without examples, ideas may often seem vague, impersonal, and unemotional. With examples, ideas become specific, personal, and vivid, producing arousing feelings. While Crs convey first-hand experiences, evaluations, and opinions of single or few consumers, Prs present more abstract and impersonal information, as such information is based on statistical evaluations of a massive amount of preference data.

Based on these differing mood-relevant cues, it can be argued that Crs are more likely to stimulate affective responses than Prs [21, 68]. Similarly, given that Crs often comprise elements of stories (e.g., plot, characters, drama), Crs may have a greater ability to generate empathy among users and thus affect consumers via direct emotional “contagion” [22]. In this regard, enthusiasm expressed in Crs de-scribing the joys of a particular product could, for example, directly generate some similar feelings in the minds of the readers [18]. Due to the emotive text quality of Crs, consumers can thus become emotionally “immersed” in the e-commerce Web site [40]. In contrast, on most e-commerce Web sites, Prs deliver statistical data (e.g., “51 percent that viewed this item also bought it”) in conjunction with


key product attributes (e.g., price, key features) that are clearly arranged and easy to perceive. Further, Prs do not use emotive text nor present product information in a personal story format, which suggests that Prs evoke less perceived affective quality for consumers than Crs. Therefore,

Hypothesis 3: Consumers will perceive greater perceived affective quality of CRs than of PRs.

In a growing body of IS research, trust has been integrated as an important antecedent to technology acceptance [14, 25, 60]. More specifically, empirical studies of trusting beliefs in an e-commerce vendor and in OPrs reveal that trust plays an important role in directly or indirectly (e.g., via perceived usefulness or perceived ease of use) affecting consumers’ usage intentions [73, 76]. Trust in OPrs can be considered an extension of interpersonal trust, because, according to the theory of social responses to computers [77], people treat technological artifacts as social actors and build trust and relationships with them [98]. When consumers form their initial trusting beliefs in OPr, the perceived quality of the information provided in OPr contributes to the cognitive evaluation of the OPr’s trustworthiness. Users make inferences about the OPr’s trustworthiness by reflecting on issues such as the amount and scope of explana-tory information it provides, or on how well the recommended products conform to the preference structure users have specified. Users’ trusting beliefs in OPrs can be enhanced when OPrs provide additional information in the form of explanations (e.g., how, why, and trade-off explanations) to reveal their underlying reasoning process and cognitively justify their recommendations [98].

These findings can be extended to the differential effects of Prs and Crs on trust-ing beliefs. We argue that Crs are more likely to evoke higher trusting beliefs in consumers than Prs. Indeed, Crs on e-commerce Web sites typically include first-hand experiences and explanations of other users that report on how and why they have (or have not) bought the product, thereby unfolding their reasoning process. Moreover, Crs let users learn about the reasoning process of other users from which they can infer whether the product is suitable or not. Irrespective of the relevance or irrelevance of the product recommendation for them, users can follow the argument by which another user made his or her decision. This transparency of the reasoning process is a key characteristic of Crs and may contribute to building users’ trust [1]. In contrast, the majority of Prs typically lack adequate explanation facilities [98]. They provide an explanation for suggesting a product only in the sense that they pre-sent concrete product alternatives based on a user’s or other users’ preferences or past behavior. however, Prs typically lack information and concrete explanations on how the product can be used or why a product might be suitable. This prevents Prs from revealing the underlying reasoning process that governs their decision making and thus prevents them from demonstrating the competence, benevolence, and integrity of the OPr. Empirical studies in the e-commerce OPr literature has also found that this kind of information asymmetry (i.e., that a Pr has more information than the user with respect to the underlying logic of the Pr’s recommendation) hampers consumer trust toward the Pr [98].


In addition to product-related information and explanations, other sources are also used by consumers to form their trusting beliefs in OPrs, especially by those who have limited product knowledge and therefore cannot accurately appraise the com-pleteness and integrity of the information provided by the OPr. As we compare Prs and Crs on their differential effects on trusting beliefs, reflecting on the source cred-ibility (i.e., the credibility an OPr conveys with the presented recommendation) may have particular explanatory value. research on source credibility and source effects of communication has a long tradition in communication, consumer, and marketing research. hovland and Weiss [47], for example, showed that the communicator’s credibility, attractiveness, physical appearance, familiarity, and power, all of which are attributes of the information source, can have an impact on the credibility of the message. Past studies also indicate that source credibility determines the effective-ness of a communication in the offline world and that audiences’ attributions of a source’s intentions are a key factor in the perception of trustworthiness [31]. People tend to believe information from a highly credible source and more readily accept the information; conversely, if the source has low credibility, the receiver is less likely to accept that information [89].

The effect of source credibility is also believed to apply to the online environment. Wathen and Burkell [99] found, for example, that Web information receivers consider virtual source credibility as an important indicator of information credibility. The recommendation source may especially be relevant when comparing Prs and Crs. By definition, Crs include other users’ opinions and accounts of personal product experiences and are likely to be judged to emanate from trustworthy sources because their authors are fellow consumers who may share similar interests and may have used the product in a real-world setting. Conversely, as Prs are produced by the e-commerce vendor, they are more likely to be perceived to have a vested interest in promoting the product to increase sales [21], which in turn may decrease consumers’ trusting beliefs in the recommendation. Furthermore, Prs may be considered as manipulative in the sense that only one-sided, and always positive, recommendations are presented. By contrast, one or a collection of Crs may include multisided messages (e.g., positive, neutral, negative) and thus present more complete information, which is likely to be perceived as more credible [22]. As such,

Hypothesis 4: Consumers will have higher trusting beliefs in CRs than in PRs.

The Moderating role of Product Type: Search Versus Experience Goods

Given that usage behavior, task performance, and decision outcomes change as product type changes [40, 63], this study examines the moderating effect of product type on the relationships between OPr use and the different consumer beliefs. Product type has been studied extensively in decision-making research, where it has frequently been categorized into search and experience goods based on the possibility for consumers to assess the key qualities of a product before purchasing and consuming it [17, 64,


87]. According to Nelson [64], perceived quality of a search good involves attributes of an objective nature, whereas perceived quality of an experience good depends more on subjective attributes that are a matter of personal taste. Weathers et al. [100] categorize goods according to whether it is necessary to go beyond simply reading information to also use one’s other senses (e.g., listen online to a 30-second clip from a music CD) to evaluate quality. The evaluation of search goods is primarily associated with a fact-gathering, knowledge-seeking stance that is typically outcome oriented, concentrated, impersonal, and objective [65]. But the evaluation of experience goods is, rather, comparable to an engaging expedition that is process oriented, personal, and subjective [86].

In light of these studies, search goods can be characterized as products that can easily be evaluated and compared based on objective key attributes before making a purchase decision. While the evaluation of search goods can be supported by ex-periential elements (e.g., photos or videos demonstrating the ease of use of a digital video recorder),2 there is no strong need to use other than visual senses to evaluate quality [63]. Further, it is rather difficult for consumers to assess experience goods before the consumption of the product, as user preferences are formed during rather than before product consumption [87]. Experience goods refer to products in which it is relatively difficult and costly to obtain information on quality prior to interacting with the product. In addition, key attributes of experience goods are rather subjective or difficult to compare, and there is often the need to use one’s senses to evaluate quality. As a consequence, experience goods require sampling (e.g., movie trailers, software trial versions) or purchase in order to evaluate product quality [63]. Typical examples of search goods include furniture [65] and calculators [87]; examples of experience goods include music [17] and wine [54]. Although all products involve a certain mix of search and experience attributes, the categorization of search and experience goods continues to be relevant and widely accepted [49].

The difference between search and experience products can inform our understanding about the effectiveness of OPr types when considering different product types. When evaluating OPrs, different instrumental, affective, and trusting beliefs are elicited that influence consumers’ preferences. More specifically, there may be an interaction between product type and OPr type, as different product types have differing informa-tion needs and thus trigger different instrumental, affective, and trusting processes [74] that can be met more or less effectively by different OPr types.

Prs on e-commerce Web sites most often provide a lean and well-organized infor-mation design with objective key product attributes and statistical data about other consumers’ evaluation and buying behavior (e.g., “80 percent buy the item featured on this page”) being at the center of their recommendation. Thus, compared to Crs, they are more effective at more rapidly providing an overview of key product attributes without including much noise that is irrelevant to the objective product evaluation. Given that search goods, as opposed to experience goods, require that objective at-tributes be evaluated in an outcome-oriented and impersonal fashion, we argue that Prs may better match the information needs of search goods [3]. As such,


Hypothesis 5a: Product type moderates the effect of OPR use on users’ perceived usefulness. Use of PRs will elicit greater perceived usefulness in the context of search goods than in the context of experience goods.

As discussed above, taste, emotions, and subjective attributes play a larger role in the evaluation of experience goods (e.g., movies or music) than in the evaluation of search goods [49]. Because it is difficult or even impossible to evaluate experience products before purchase, consumers are usually more inclined to trust and rely on OPrs for such products [53]. Given that Crs include the experiences and opinions of other consumers, we argue that they better match the information needs for experi-ence goods in terms of providing a transparent reasoning process underlying product acquisition. Similarly, we believe that Crs can better fit the information needs of experience goods in terms of eliciting consumers’ affective reactions. As previously discussed, Crs most often contain emotive text (e.g., in a story or recounting), leading consumers to become emotionally immersed and engaged in the process of product evaluation. Because experience goods, as opposed to search goods, usually require representations that facilitate more in-depth and personal product evaluations, we ar-gue that Crs may be more effective in stimulating affective responses for experience goods rather than for search goods. As such,

Hypothesis 5b: Product type moderates the effect of OPR use on users’ trusting beliefs. Use of CRs will elicit greater trusting beliefs in the context of experience goods than in the context of search goods.

Hypothesis 5c: Product type moderates the effect of OPR use on users’ perceived affective quality. Use of CRs will elicit greater perceived affective quality in the context of experience goods than in the context of search goods.

Antecedents of Consumers’ OPr reuse and Purchase Intentions Based on OPr Use

While much OPr research has focused on the direct effects of OPr use on decision outcome variables [41, 42, 91], recent studies introduced the mediating effects of the decision process between OPr use and decision outcomes [52]. This is consistent with the theory of planned behavior and the TAM, which both state that the effect of IT (in its various forms and derivatives) on behavioral intention is mediated by behavioral beliefs (i.e., perceived usefulness and perceived ease of use) toward the behavior [4, 27]. We expect the same to hold for the effect of Prs and Crs on behavioral beliefs in the context of e-commerce platforms.

Several IS acceptance studies have shown that the set of factors (i.e., the different consumer beliefs) representing the decision-making process in this study has a direct effect on behavioral intentions (for perceived usefulness, see [2, 27, 94]; for trusting beliefs, see [35, 73, 97]; for perceived affective quality, see [85, 107]). We use intention to reuse the OPr and intention to purchase a product based on the OPr as measures


of the impact of OPr use on the decision outcomes. Both outcome factors have been extensively used in business-to-consumer e-commerce research [37, 56]. Further, the theory of planned behavior suggests that behavioral beliefs such as the decision process variables examined in this paper mediate the effect of external variables on intentions. Thus, we expect that the impact of OPr evaluation on reuse and purchase intentions will be mediated by this set of decision process variables. More specifi-cally, we argue that stronger instrumental, affective, and trusting beliefs in OPrs will increase the likelihood that consumers reuse (i.e., continue paying attention to and leverage) OPrs in subsequent product search and evaluation activities. likewise, we argue that higher perceived usefulness, perceived affective quality, and trusting beliefs in OPr will translate into increased intentions to purchase products recommended by the OPr due to positive evaluations spilling over from instrumental, affective, and trusting cues of OPr [11, 52, 58, 73, 93]. As such,

Hypothesis 6a: The effect of OPR use on intention to reuse OPR is mediated by the consumer’s perceived usefulness of OPR.

Hypothesis 6b: The effect of OPR use on intention to purchase is mediated by the consumer’s perceived usefulness of OPR.

Hypothesis 7a: The effect of OPR use on intention to reuse OPR is mediated by the consumer’s perceived affective quality of OPR.

Hypothesis 7b: The effect of OPR use on intention to purchase is mediated by the consumer’s perceived affective quality of OPR.

Hypothesis 8a: The effect of OPR use on intention to reuse OPR is mediated by the consumer’s trusting beliefs of OPR.

Hypothesis 8b: The effect of OPR use on intention to purchase is mediated by the consumer’s trusting beliefs of OPR.

For replication purposes, we also reexamine the relationships between perceived ease of use and perceived usefulness [27, 97], trusting beliefs and perceived usefulness [35, 73, 97], and perceived affective quality and perceived usefulness [85, 107].

research Method

Study Design and Context

The reseArch model depicTed in figure 1 was tested via a laboratory experiment in a 2 × 2 between-subjects factorial design. Two types of online product recommendations (Pr, Cr) in conjunction with two different product types (search product: calcula-tors; and experience product: music CDs) were manipulated between subjects [39]. A total of 396 subjects were recruited via e-mail from a German panel of online users (called “Socio-Scientific Panel”) maintained by oFb, an open, research-based online survey institution. We chose calculators and music CDs as our products for three main reasons: (1) calculators have been used as typical search products, and music


CDs as typical experience products in previous research studies [17, 57, 87]; (2) both products have comparable price ranges, are nonessential, and similarly appeal to both female and male users; and (3) both products have a relatively high number of product attributes and a large number of alternatives available on the market, which requires a certain level of know-how from consumers, making the use of OPr more relevant on e-commerce Web sites [76].

Amazon.com was chosen as the study context because it is recognized as one of the leading e-commerce retailers and is a positive example for other online stores in terms of the way it supports the provision of Prs and Crs [8, 33]. Similar to previ-ous online recommendation studies [57], we developed a Java-based software agent (called “AmaFilter”) for the Amazon.com Web services environment that intercepted the Web pages sent by Amazon.com and filtered the content to randomly generate Prs and Crs in order to make the different treatments as realistic as possible. An online survey platform was used to present the instructions, the filtered Amazon.com Web pages with Prs and Crs, as well as the pre- and postexperimental questionnaires that had been pretested with a sample of 24 Amazon.com users.

Experimental Procedures, Manipulations, and Incentives

The experiment proceeded as follows. An introduction to the study’s context was pre-sented on the online survey platform. Participants were generally told that Amazon.com was planning to overhaul some features on its Web site (including design, structure, content, and functionality of the Web site) and that the study was designed to evaluate customers’ experiences with these features and their overall usage behavior during a shopping task (i.e., choosing a product item for a good friend). Participants were further instructed to assume that they had happened to come across several Web pages on Amazon.com that provide specific Web site features and that the following information would be all the information available for further evaluations on Amazon.com. There was no time limit for the tasks. After completing an online preexperimental question-naire containing questions on the subjects’ demographic information (i.e., age, gender, education, household income, familiarity with and usage of Amazon.com, Internet usage, and online shopping experience), participants were redirected to a simple de-fault Amazon.com home page provided by AmaFilter. Given that we expected some variability in the level and context of participants’ online shopping experiences, we created a simple default Amazon.com home page showing a list of “New and Future releases” of different product items (calculators and music CDs) and instructed all the subjects to evaluate3 several recommendation pages (including Web sites with both Pr and Cr) that could be accessed from the default home page [57]. This was done to allow participants to establish a common frame of reference and start with a baseline experience, and is consistent with helson’s argument associated with adaptation-level theory [43]. Further, we used this baseline task to collect individual preference data of subjects (i.e., preference profiles) for later use during the experimental tasks in order to generate appropriate Prs [15]. This step took 8–16 minutes based on the log files we analyzed after the experiment. Once they finished the baseline task, the subjects


were randomly assigned4 to one of the four groups pertaining to our 2 × 2 between-subjects factorial design5 (see Table 2).

In their respective experimental conditions, the subjects were then instructed6 to browse the Web site and to locate information on the presented products to become familiar with the Web site layout and its recommendation features. After having inspected several product pages, the participants were instructed to make a purchase decision for a specific product item (among the available products) that should be sent as a gift to a friend.7 Finally, each subject was asked to complete a postexperimental online questionnaire that recorded his or her evaluations of the OPr features (i.e., perceived usefulness, perceived ease of use, perceived affective quality, and trusting beliefs) and behavioral intentions. The postexperimental questionnaire also included questions for several manipulation checks.

The provision of Pr on the experimental Web sites included a set of recommenda-tions (usually a list of four or five similar product items) that were generated based on a user-based collaborative-filtering method.8 Following this method, the subject’s preference data collected during the baseline experience task was used to automati-cally calculate similarities between the subject’s profile and the preference profiles of other users. Based on these data similarities (i.e., preference proximity), other product items of like-minded consumers were suggested to the subjects [15]. The Prs on the experimental Web sites were prefaced by phrases such as “Customers who bought this title also bought . . .” and “What do customers ultimately buy after viewing this item?” By selecting a specific product item, subjects could access the provider-generated product descriptions and attributes on this recommended item to evaluate the product features. As in Kumar and Benbasat [57], support for Crs on the experimental Web sites was provided by displaying a randomly created sequence of consumer reviews pertaining to a specific product item when the user clicked or moved the mouse over the product item (which he or she could access via searching or browsing the product Web sites). To control for review valence and consistent with previous studies [70], the reviews included a random mix of positive, negative, and neutral feedback across all subjects in the Cr conditions. See Figure 2 for samples of our experimental Web sites with Prs and Crs.

Table 2. Group Assignment

Type of online product recommendation

Product type

Search good (SG)

Experience good (EG)

Provider recommendation (Pr)

(1) PR × SG (n = 103)

(2) PR × EG (n = 98)

Consumer review (Cr) (3) CR × SG (n = 99)

(4) CR × EG (n = 96)


Figure 2. Examples for Experimental Web Sites Including Prs for Calculators (top) and Crs for Music CDs (bottom)


To provide an incentive for the subjects to evaluate the products, the subjects were told that they would be entered into a raffle where they could win the music CD or the calculator they had selected for their friends. As in other experimental studies (e.g., [76]), providing extra incentives is very helpful in motivating participants to view the experiment as a serious online shopping session and to increase their involvement.

Survey Instrument and Measurement Models

The survey instrument, which had been translated into German and back-translated into English by a professional translation services firm, used validated scales for all the constructs, with minor wording changes (see Table 3).9 Measures for perceived affective quality were adapted from Zhang and li [107] and measures for trusting beliefs in OPr were adapted from Wang and Benbasat [97]. Consistent with previ-ous empirical studies (e.g., [76]), we used trusting beliefs as an integrated construct comprising all three subdimensions identified in the literature (i.e., competence, be-nevolence, and integrity). Because the study focused on the differential effects of Prs and Crs on trusting beliefs as a whole, we did not further specify the relationships to trusting beliefs’ subdimensions. Measures for perceived usefulness were adapted from Davis [27]. Measures for intentions to reuse and intentions to purchase were adapted from prior literature [52, 56]. All the questionnaire items were measured on likert-type scales, anchored at 1 = “strongly disagree,” 4 = neutral, and 7 = “strongly agree.” As in Kamis et al. [52], two binary variables were constructed (i.e., OPr use = 1 for Prs, OPr use = 0 for Crs; Product type = 1 for search products, Product type = 0 for experience products) to capture the four experimental group conditions.

Data Analysis

Sample Descriptives

As shown in TABle 4, our sample can be considered as representative of the entire online customer population in Germany [96]. Almost two-thirds of our subjects were between the ages of 20 and 40, 58 percent are female, 68 percent have at least some college/university education, and 64 percent have a household income of over €30,000 per year. In the preexperiment questionnaire, participants were also asked about their experience and familiarity with Amazon.com, whether they would regularly visit the Amazon.com Web site, and about their Internet usage and online shopping habits. On average, participants spent 18.3 hours per week using the Internet and had shopped online 10.4 times on average in the preceding 6 months.

Nonresponse bias was assessed by verifying that early and late respondents were not significantly different [10]. We compared both samples based on their sociodemo-graphics. t-tests between the means of the early (first 50) and late (last 50) respondents showed no significant differences (p > 0.05), indicating that nonresponse bias was unlikely to have affected the results.


Tabl

e 3.

Sur

vey

Inst

rum

ent a

nd D

escr

iptiv

e St

atis

tics

Con

stru

ct a

nd in

dica

tors

(s

cale

rel

iabi

lity

and

AV

E)

Des

crip

tives

and

st

anda

rdiz

ed f

acto

r lo

adin

gs

I2R

Inte

ntio

n to

reu

se (

Cro

nbac

h’s

α =

0.8

47, C

R =

0.8

97, A

VE

= 0

.685

)M

ean

= 5

.18,

SD

= 0

.83

If yo

u ne

eded

to p

urch

ase

a si

mila

r pr

oduc

t in

the

futu

re, h

ow li

kely

is it

that

. . .

I2

R1

. . .

you

wou

ld in

tend

to c

ontin

ue u

sing

this

type

of O

PR

in th

e fu

ture

?0.

827

I2R

2. .

. yo

u w

ould

pre

dict

you

r us

e of

this

type

of O

PR

to c

ontin

ue in

the

futu

re?

0.84

2I2

R3

. . .

you

plan

to c

ontin

ue u

sing

this

type

of O

PR

in th

e fu

ture

?0.

792

I2R

4. .

. yo

u w

ould

con

tinue

to p

ay a

ttent

ion

to th

is ty

pe o

f OP

R?

0.84

8I2

PIn

tent

ion

to p

urch

ase

(N/A

) M

ean

= 5

.44,

SD

= 0

.84

I2P

If yo

u ac

tual

ly h

ad th

e m

oney

, how

like

ly is

it th

at y

ou w

ould

buy

the

sele

cted

pro

duct

rec

omm

ende

d on

the

prev

ious

Web

site

s?N

/A

PU

Per

ceiv

ed u

sefu

lnes

s (C

ronb

ach’

s α

= 0

.804

, CR

= 0

.872

, AV

E =

0.6

30)

Mea

n =

4.5

3, S

D =

0.8

1P

U1

Usi

ng th

is ty

pe o

f OP

R e

nabl

es m

e to

find

pro

duct

s I w

ant m

ore

quic

kly.

0.79

6P

U2

Usi

ng th

is ty

pe o

f OP

R e

nhan

ces

my

effe

ctiv

enes

s in

find

ing

suita

ble

prod

ucts

.0.

817

PU

3If

I use

this

type

of O

PR

, I w

ill in

crea

se th

e qu

ality

of m

y ju

dgm

ents

.0.

771

PU

4U

sing

this

type

of O

PR

allo

ws

me

to a

ccom

plis

h m

ore

anal

ysis

than

wou

ld o

ther

wis

e be

pos

sibl

e.0.

791

PE

OU

Per

ceiv

ed e

ase

of u

se (

Cro

nbac

h’s

α =

0.8

68, C

R =

0.9

10, A

VE

= 0

.716

)M

ean

= 5

.01,

SD

= 0

.90

PE

OU

1Le

arni

ng to

use

this

type

of O

PR

wou

ld b

e ea

sy fo

r m

e.0.

837

PE

OU

2M

y in

tera

ctio

n w

ith th

is ty

pe o

f OP

R is

cle

ar a

nd u

nder

stan

dabl

e.0.

844

PE

OU

3It

wou

ld b

e ea

sy fo

r m

e to

bec

ome

skill

ful a

t usi

ng th

is ty

pe o

f OP

R.

0.85

0P

EO

U4

I find

this

type

of O

PR

eas

y to

use

.0.

853 (c

onti

nues

)


Con

stru

ct a

nd in

dica

tors

(s

cale

rel

iabi

lity

and

AV

E)

Des

crip

tives

and

st

anda

rdiz

ed f

acto

r lo

adin

gs

TB

Trus

ting

belie

fs (

Cro

nbac

h’s

α =

0.9

26, C

R =

0.9

38, A

VE

= 0

.627

)M

ean

= 4

.68,

SD

= 0

.90

TB

1T

his

type

of O

PR

was

com

pete

nt in

rec

omm

endi

ng <

prod

uct t

ype>

.0.

829

TB

2T

his

type

of O

PR

per

form

ed it

s ro

le o

f rec

omm

endi

ng <

prod

uct t

ype>

ver

y ef

fect

ivel

y.0.

804

TB

3O

vera

ll, th

is ty

pe o

f OP

R s

uppo

rted

me

to fi

nd s

uita

ble

<pr

oduc

t typ

e>.

0.79

1T

B4

I bel

ieve

that

this

type

of O

PR

’s d

ealin

gs w

ith m

e w

ere

in m

y be

st in

tere

st.

0.77

8T

B5

Thi

s ty

pe o

f OP

R’s

dea

lings

with

me

felt

like

it w

ould

do

its b

est t

o he

lp m

e.0.

784

TB

6I b

elie

ve th

is ty

pe o

f OP

R w

as tr

uthf

ul to

me.

0.79

3T

B7

I wou

ld c

hara

cter

ize

this

type

of O

PR

’s d

ealin

gs w

ith m

e as

hon

est.

0.78

2T

B8

Thi

s ty

pe o

f OP

R a

ppea

red

to b

e un

bias

ed.

0.77

1T

B9

Thi

s ty

pe o

f OP

R is

sin

cere

and

gen

uine

.0.

792

PAQ

Per

ceiv

ed a

ffect

ive

qual

ity (

Cro

nbac

h’s

α =

0.9

76, C

R =

0.9

78, A

VE

= 0

.685

)M

ean

= 4

.55,

SD

= 1

.05

Ple

ase

rate

how

acc

urat

ely

each

wor

d de

scrib

es th

e O

PR

you

use

d.A

rous

al q

ualit

y (P

AQ

A)

PAQ

A1

inte

nse

(0.8

59)

PAQ

A2

arou

sing

(0.

842)

PAQ

A3

activ

e (0

.859

)PA

QA

4 al

ive

(0.8

66)

PAQ

A5

forc

eful

(0.

895)

PAQ

A: α

= 0

.915

CR

= 0

.937

AV

E =

0.7

47M

ean

(SD

) =

4.5

7 (1

.05)

Tabl

e 3.

Con

tinue

d


Sle

epy

qual

ity (

PAQ

S, r

ever

sed)

PAQ

S1

inac

tive

(0.8

80)

PAQ

S2

drow

sy (

0.85

3)PA

QS

3 id

le (

0.87

3)PA

QS

4 la

zy (

0.85

5)PA

QS

5 sl

ow (

0.88

8)

PAQ

S: α

= 0

.920

CR

= 0

.940

AV

E =

0.7

57M

ean

(SD

) =

4.5

3 (1

.05)

Ple

asan

t qua

lity

(PA

QP

)PA

QP

1 pl

easa

nt (

0.86

1)PA

QP

2 ni

ce (

0.86

2)PA

QP

3 pl

easi

ng (

0.86

0)PA

QP

4 pr

etty

(0.

850)

PAQ

P5

beau

tiful

(0.

847)

PAQ

P: α

= 0

.909

CR

= 0

.932

AV

E =

0.7

33M

ean

(SD

) =

4.5

5 (1

.06)

Unp

leas

ant q

ualit

y (P

AQ

U, r

ever

sed)

PAQ

U1

diss

atis

fyin

g (0

.834

)PA

QU

2 di

sple

asin

g (0

.831

)PA

QU

3 re

puls

ive

(0.8

51)

PAQ

U4

unpl

easa

nt (

0.92

5)PA

QU

5 un

com

fort

able

(0.

923)

PAQ

U: α

= 0

.922

CR

= 0

.942

AV

E =

0.7

64M

ean

(SD

) =

4.5

4 (1

.06)

Not

es:

Cr

= c

ompo

site

rel

iabi

lity;

AV

E =

ave

rage

var

ianc

e ex

trac

ted;

SD

= s

tand

ard

devi

atio

n; N

A =

not

app

licab

le. F

or P

AQ

, sta

ndar

dize

d fa

ctor

load

ings

are

de

pict

ed in

par

enth

eses

rig

ht b

ehin

d th

e in

dica

tors

of

the

five

subd

imen

sion

s.


Table 4. Descriptives

VariableFrequency (percent)

Mean (standard deviation)

Age (years)Under 20 35 (8.8)20-29 122 (30.8)30-39 132 (33.3)40-49 77 (19.4)Over 49 30 (7.6)

GenderMale 166 (41.9)Female 230 (58.1)

EducationGrammar/elementary school 8 (2.0)High school or equivalent 77 (19.4)Some college/university 68 (17.2)Bachelor’s degree 79 (19.9)Diploma/master’s degree 95 (24.0)Doctoral degree 25 (6.4)Other 32 (8.1)Prefer not to say 12 (3.0)

Familiarity with Amazon.com (five-point Likert scale)*

Visit Amazon regularly 3.33 (0.41)Familiar with Amazon 4.03 (0.93)

Internet usage and online shopping habits

Internet usage hours per week 15.3 (4.5)Online shopping frequency in last 6 months

10.4 (2.3)

Household IncomeLess than e10.000 12 (3.0)e10.000–e19.999 49 (12.4)e20.000–e29.999 49 (12.4)e30.000–e39.999 65 (16.4)e40.000–e49.999 73 (18.4)e50.000–e74.999 86 (21.7)e75.000–e99.999 20 (5.1)More than e100.000 8 (2.0)Rather not say 34 (8.6)

* Anchored at 1 = “strongly disagree” and 5 = “strongly agree.”


Controls and Manipulation Checks

To confirm the random assignment of subjects to the different experimental condi-tions, we performed a multivariate analysis of variance (MANOVA). There were no significant differences in gender (F = 1.573, p = 0.195), age (F = 0.498, p = 0.684), education (F = 0.183, p = 0.908), household income (F = 0.577, p = 0.631), familiarity with Amazon.com (F = 0.145, p = 0.933), usage of Amazon.com (F = 0.153, p = 0.928), Internet experience (F = 0.996, p = 0.349), online shopping behavior (F = 0.782, p = 0.505), and personal relevance of product types (F = 1.345, p = 0.271) among the four experimental conditions. These results indicate that participants’ characteristics were not the cause of the differences in consumers’ beliefs and intentions.

Several further manipulation checks were performed. All of the 396 subjects clicked on at least 8 hyperlinks to access the online recommendations on the product Web sites, indicating that all the subjects were exposed to the OPr treatments.10 In addi-tion, the subjects were asked in the postexperimental questionnaire to what extent they had perceived changes (i.e., in design, structure, content, or functionality) to the Web sites they had visited during the experiment (seven-point likert scale anchored at 1 = “low” and 7 = “high”). All four treatment groups recognized these general changes to the Web sites, and there were no significant differences between the four groups (the means ranged between 5.42 and 5.62; F = 1.44, p = 0.233). In addition, drawing on items11 from Kumar and Benbasat [57], we checked participants’ per-ceived support for Prs and Crs in the respective conditions of the experiment. t-test results indicated that our experimental manipulations regarding the provision of the different OPrs were successful.12 Finally, 100 percent of the participants assigned to the four treatments also recalled the combinations of recommendation source and product type correctly in the postexperimental questionnaire. Taken together, these results indicate that the treatments were successfully executed. Given that all of our items were measured with the same method, we tested for common method variance using harman’s one-factor test [75]. We performed an exploratory factor analysis on all the variables, but no single factor was observed and no single factor accounted for a majority of the covariance in the variables. Further, a correlational marker technique was used, in which the highest variable from the factor analysis was entered as an additional independent variable [79]. This variable did not create a significant change in the variance explained in the dependent variables. Both tests suggest that common method bias is unlikely to have significantly affected our results.

Measurement Characteristics

The reflective first-order measurement models and second-order measurement model (i.e., perceived affective quality) were validated using recommended validation proce-dures [24]. The items of scales in a related domain were pooled and factor analyzed to assess their convergent and discriminant validity. While convergent validity was determined both at the individual indicator level and at the specified construct level, discriminant validity was assessed by analyzing the average variance extracted (AVE)


and interconstruct correlations [24, 34]. All the standardized factor loadings were significant (p < 0.05), thus providing evidence of convergent validity. Construct reli-ability was assessed by computing the composite reliability for each construct. All the constructs had a composite reliability above the cutoff value of 0.70 [12]. Further, all of the reflective constructs met the threshold value for the AVE (> 0.50). Discriminant validity was assessed by verifying that the square roots of AVEs exceeded intercon-struct correlations (see Table 5). The same validation procedures were applied to the measurement models of Pr and Cr subsamples in both studies. All of the constructs in these measurement models also satisfied the reliability and validity criteria mentioned above (presentation of these measurement models is omitted here for brevity). The factor loadings, values for composite reliability and AVE, and descriptive statistics of all constructs can be seen in Table 3.

Test of hypotheses

The model was tested via partial least squares (PlS) analysis using SmartPlS 2.0 with the bootstrapping resampling procedure [81]. As shown in Figure 3, consumers were found to perceive significantly greater perceived usefulness and perceived ease of use of OPrs with Prs than with Crs as indicated by the positive and significant beta coefficients, thus supporting h1 and h2. Conversely, consumers were found to perceive significantly greater perceived affective quality and trusting beliefs of OPrs with Crs than with Prs as shown by the negative and significant beta coefficients, thus supporting h3 and h4.

The moderating effects of product type were assessed by examining the beta coef-ficients between product type and the three belief categories. First, while the beta coefficient between product type and perceived usefulness is positive and significant (β = 0.083, p < 0.05), it is not significant for the relationship between product type and perceived ease of use (β = 0.012, p > 0.05). Further, the beta coefficients between product type and trusting beliefs (β = –0.132, p < 0.001) and between product type and perceived affective quality (β = –0.194, p < 0.05) are both negative and significant. Second, the beta coefficients between the interaction term (OPr use × Product type) and the three belief categories are all positive and significant (β = 0.471, p < 0.001; β = 0.379, p < 0.001; β = 0.491, p < 0.001), indicating that product type significantly moderates the relationships between OPr use and the three belief categories. More specifically, Prs’ effects on perceived usefulness are reinforced in the context of search products (as compared to experience products), while Crs’ effects on trusting beliefs and perceived affective quality are strengthened in the context of experience products (as compared to search products), hence supporting h5a, h5b, and h5c. As shown in Figure 4, the estimated means of perceived usefulness, perceived ease of use, trusting beliefs, and perceived affective quality were plotted for each of the two product types. The results show that Prs are perceived as more useful for search goods than for experience goods. likewise, trusting beliefs and perceived affective quality toward Prs increase when moving from an experience good to a search good.


Tabl

e 5.

lat

ent V

aria

ble

Cor

rela

tion

Mat

rix

lat

ent c

onst

ruct

12

34

56

78

Inte

ntio

n to

reu

se0.

828

Inte

ntio

n to

pur

chas

e0.

461

1.00

0Tr

ustin

g be

liefs

0.58

70.

434

0.79

2P

erce

ived

use

fuln

ess

0.21

90.

195

–0.1

610.

794

Per

ceiv

ed e

ase

of u

se0.

016

0.08

6–0

.420

0.68

90.

846

Per

ceiv

ed a

ffect

ive

qual

ity0.

571

0.43

10.

478

–0.2

35–0

.531

0.82

8

OP

R u

se–0

.075

–0.0

29–0

.557

0.59

00.

742

–0.6

751.

000

Pro

duct

type

–0.1

34–0

.080

–0.1

35–0

.040

0.08

2–0

.197

0.00

51.

000

Not

e: D

iago

nal e

lem

ents

in b

oldf

ace

are

the

squa

re r

oot o

f av

erag

e va

rian

ce e

xtra

cted

(A

VE

). T

hese

val

ues

shou

ld e

xcee

d in

terc

onst

ruct

cor

rela

tions

(of

f-di

agon

al

elem

ents

) fo

r ad

equa

te d

iscr

imin

ant v

alid

ity.


Conversely, Crs elicit higher trusting beliefs and perceived affective quality in an experience good than in a search good.

The mediating role of user evaluation variables (i.e., different consumer beliefs) between OPr use and the two behavioral intention variables was assessed by per-forming a Sobel’s test [90]. We ran two independent PlS models to generate the required path coefficients and standard errors [66]. The first model included paths from OPr use to the three mediator variables. The second model included paths from the mediator variables to the two behavioral intention variables, as well as paths from OPr use to the behavioral intentions variables. results show that the effects of OPr use on intentions to reuse the OPr and intentions to purchase were significantly mediated by trusting beliefs (Sobel

I2r = –2.177, p < 0.05; Sobel

I2P = –1.970, p < 0.05)

and perceived affective quality (SobelI2r

= –2.844, p < 0.01; SobelI2P

= –2.476, p < 0.05), while they were not significantly mediated by perceived usefulness (So-bel

I2r = 1.008, p > 0.05; Sobel

I2P = 1.017, p > 0.05). Given the significant direct path

from OPr use to intentions to reuse the OPr (β = 0.198, p < 0.01) and intentions to purchase (β = 0.171, p < 0.001), it appears that trusting beliefs and perceived affective quality partially mediate the effects of OPr use on intentions to reuse the OPr and intentions to purchase. In sum, h7a, h7b, h8a, and h8b are supported, while h6a and h6b are rejected.

As shown in Figure 5, the effect of the three individual beliefs’ categories on be-havioral intentions was assessed for each OPr type subsample. In the Cr subsample, trusting beliefs and perceived affective quality were found to be the most prevalent consumer beliefs affecting intentions to reuse and intentions to purchase as indicated by their significant beta coefficients and effect sizes. By contrast, perceived useful-

Figure 3. results

* p < 0.05; ** p < 0.01; *** p < 0.001.


Figure 4. Means of Perceived Usefulness, Perceived Ease of Use, Trusting Beliefs, and Perceived Affective Quality

ness was found to be the dominant belief influencing intentions to reuse in the Pr subsample, while intentions to purchase were equally affected by perceived usefulness and perceived affective quality.

Discussion

The mAin oBjecTive of This pAper wAs To unrAvel the distinct effects of Prs and Crs in e-commerce transactions and to compare the relative impact of two sources of e-commerce OPrs in two different product contexts (i.e., search products versus experience products). Theoretically, our findings provide a holistic understanding of the mechanisms by which different OPr types affect instrumental, affective, and trusting beliefs. Practically, the findings are potentially useful to managers who wish to design sales-efficient e-commerce Web sites that enhance online consumers’ overall shopping experience.

Specifically, this study provides a finer-grained understanding of the impact of Pr and Cr on consumers’ perceived usefulness, perceived ease of use, trusting beliefs, and perceived affective quality of OPr and how these different types of consumer


beliefs translate into intentions to reuse the OPr and to purchase based on the OPr. Other studies have either examined the effects of different OPr types on single evalu-ation criteria (e.g., sales [71, 72] or social presence [44, 76]) or the effects of one OPr type on many evaluation criteria (e.g., different components of trusting beliefs [98]). Although such findings are important, the e-commerce OPr literature had not yet theorized about how Prs and Crs differ on their impact on instrumental, affective, and trusting consumer beliefs. By drawing upon various research disciplines, this study provides new theoretical perspectives that expand our understanding regarding the ef-fect of Prs and Crs on continued usage of online recommendations and on individual shopping behavior. Notably, our results demonstrate that not all OPr types are equally conducive in influencing trusting beliefs, perceived affective quality, and perceived usefulness, suggesting the existence of superior effect mechanisms for different OPr types. More specifically, Crs were found to be superior to Prs in influencing con-sumers’ trusting and affective beliefs, while Prs were found to have stronger effects on instrumental consumer beliefs. Our results also show that perceived usefulness is

Figure 5. Subsample Analyses for Pr (top) and Cr (bottom)

Notes: N = 201 (top panel); n = 195 (bottom panel). Effect size f 2 is shown in parentheses. * p < 0.05; ** p < 0.01; *** p < 0.001; ns = nonsignificant.


the stronger driver of intentions to reuse OPrs in a Pr setting, while trusting beliefs and perceived affective quality were found to be stronger than perceived usefulness in affecting both intentions to reuse and to purchase in a Cr context. In sum, Prs and Crs focus on two distinct relationship-building orientations. While Prs appear to be more effective for transactional relationships, Crs appear to be more efficient for enhancing consumer experiences that build on trust and affections.

In addition, our study shows that product type exhibits a moderating effect on the relationship between OPr use and consumer beliefs. Specifically, our results show that trusting beliefs and perceived affective quality are enhanced in the context of experi-ence products as compared to search products when users base their evaluations on Crs, and that perceived usefulness is more strongly affected in the context of search products as compared to experience products when users base their evaluations on Prs. Further, our study found that the effects of OPr use on intentions to reuse and to purchase are mediated by trusting beliefs and perceived affective quality, but not by perceived usefulness.

Another important contribution of this study is related to its investigation of two key antecedents of users’ beliefs at the same time: OPr type and product type. While past studies focused on the impact of perceived usefulness and perceived ease of use on user behavior, less attention has been devoted to the study of perceived usefulness and perceived ease of use’s antecedents [13]. Also, and although much research has shown that affective and trusting beliefs are important antecedents of user behavior, little research has investigated the characteristics of different OPr types (including different interface designs and content structures) affecting such beliefs. In this regard, our study’s results add to the existing e-commerce OPr literature by providing a better understanding of how and why OPr use affects decision making.13

From a practical perspective, our results provide guidance to online retailers who wish to design effective OPrs that provide users with a comprehensive shopping experience taking into account instrumental effects together with trust and emotions. E-commerce Web site providers may benefit from this study by proposing or emphasizing different OPrs along the purchasing funnel [80], which describes the consumer–product interac-tion process as broken into four successive steps: awareness, consideration, purchase, and loyalty. Depending on their strategic orientation concerning product categories (e.g., experience versus information goods), channels (e.g., Internet versus mobile), and customer segments (e.g., younger versus older consumers), online retailers should assess which types of consumer reactions or combinations of these are most beneficial to increase sales, and also increase customer stickiness and satisfaction. Accordingly, retailers could adjust the provision of Prs and Crs on their e-commerce platform. Alternative site designs could be tested using the present study’s insights to better determine at which stage of the purchasing process Prs and Crs should be more or less emphasized on a Web site. Our results thus indicate that providing appropriate OPrs on a Web site can allow customers to enjoy their shopping experience more and perceive the system as improving their decision-making process and shopping efficiency. Consequently, customers might increase their online purchases based on the recommendations of OPrs and ultimately the reuse of OPrs for future purchases.


Some limitations to the present study also need to be acknowledged. First, although the study’s results strongly support the paper’s main argument that instrumental, af-fective, and trusting beliefs need to be considered together in order to reach a deeper understanding of individual intentions in the context of e-commerce transactions, we believe that longitudinal research will help better understand the temporal and causal relationships between the study’s constructs. Second, given the fact that our research model was tested in the context of one e-commerce Web site (i.e., Amazon.com) and involved just two artifacts for search and experience products (i.e., calculators and music CDs), we believe that additional tests that include a variety of both search (e.g., furniture, footwear) and experience (e.g., fragrances, wine) products on less well-known e-commerce Web sites would be useful to assess the generalizabilty of the study’s results. Moreover, using additional conceptualizations of product type (e.g., high- versus low-involvement products, physical versus digital products) and other, more personalized instantiations of recommendation sources (e.g., content-filtering-based provider recommendations and consumer reviews including multimedia) would yield complementary insights to those of this study. In addition, and although we controlled for several important variables (i.e., personal relevance of product types, familiarity with and usage of Amazon.com and online shopping behavior), we believe that controlling for initial informational needs of users as regards product attributes or product usage experiences would be useful to validate the effect of the different individual perceptions (i.e., perceived usefulness, perceived ease of use, perceived af-fective quality, and trusting beliefs) toward Pr and Cr found in this paper.14 Further, and while the study’s constructs exhibit good psychometric properties, we believe that additional work on the scales’ items would be useful to more systematically ensure that their wording is not biased toward either type of OPr. Finally, and since combining narrative and statistical forms of communication is believed to enhance the persuasiveness of a message [5], an interesting avenue for future research would be to examine what right mix of OPr types would achieve the optimal results at different stages of a consumer’s buying process.

Conclusion

online producT recommendATions hAve Become criTicAl Tools for customer online experience enhancement. As hypothesized, the present study found that Pr and Cr exhibit different effect pathways through which they affect OPr reuse and purchase intentions in the context of search and experience products. Theoretically, the study unravels the differential effects of Pr and Cr on three distinct core and critical in-dividual beliefs (instrumental, affective, and trusting) influencing individual online transactions. Practically, the study provides interesting insights about how Pr and Cr can be used to foster either transactional or loyalty-building relationships. It is hoped that the present study’s results will be useful to future research aiming at improving or developing OPrs capable of increasing the efficiency of e-commerce transactions and value co-creation encounters.


noTes

1. Note that Crs usually contain structured and easily distinguishable attributes such as “star ratings.” Nevertheless, we argue that consumers will still need to go through Crs’ unstructured text to reach the information that would enable them to confirm or disconfirm the rating’s ap-propriateness. Such process is then believed to reduce the effect of Crs on perceived ease of use as compared to perceived usefulness. We thank an anonymous reviewer for bringing up this point.

2. We thank an anonymous reviewer for bringing up this point.3. The evaluation of recommendation pages in the baseline task included one item of

perceived usefulness, perceived ease of use, trusting beliefs, and perceived affective quality respectively. No significant differences were found between subjects that were later assigned to the four study treatments.

4. random assignment was made using randomized integer numbers generated via random .org’s Application Programming Interface.

5. Three main reasons explain why we did not combine Pr and Cr in one of our treatments. First, the focus of our study was to unravel the distinct effects of Pr and Cr on different core and critical consumer beliefs. Second, our objective was to investigate how different product types modified the effect of Pr and Cr on those beliefs. Finally, our aim was to extend the study by Kumar and Benbasat [57] by conceptualizing and testing the effect of Pr and Cr on a larger set of instrumental, affective, and trusting beliefs in the context of two product types.

6. All instructions were depicted on the experimental Web sites.7. Given that similar tasks were successfully used in previous experimental settings [57, 98]

and that evaluation of Pr and Cr before product purchases to reduce behavioral uncertainty is a common user behavior in digital environments [104, 109], we believe that the realism of the task used in our experiment is acceptable.

8. User-based collaborative-filtering techniques collect a consumer’s preferences and compare them to (affinity) groups of people with similar preferences to suggest new product items (e.g., [105]). The recommendations given are personalized in the sense that user-based collaborative-filtering methods take into account the consumer’s and like-minded users’ prefer-ences to suggest other product items.

9. The German version of the survey is available from the authors upon request.10. A program routine was developed to record the number of clicks of the hyperlinks and

to check whether participants were exposed to specific product items.11. The reliability estimates for perceived support for Pr was α = 0.92 and for Cr was

α = 0.96.12. For conditions with actual support for Pr (n = 201): Mean difference between per-

ceived support for Pr versus Cr, t = 101.483, p < 0.000: for conditions with actual support for Cr (n = 195): Mean difference between perceived support for Pr versus Cr, t = –78.173, p < 0.000.

13. See, for example, propositions on recommendation output characteristics and product-related factors suggested by Xiao and Benbasat [104].

14. We thank an anonymous reviewer for bringing up this point.

references

1. Abrams, l.; Cross, r.; lesser, E.; and levin, D.Z. Nurturing interpersonal trust in knowledge-sharing networks. Academy of Management Executive, 17, 4 (2003), 64–77.

2. Agarwal, r., and Karahanna, E. Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24, 4 (2000), 665–694.

3. Aggarwal, P., and Vaidyanathan, r. The perceived effectiveness of virtual shopping agents for search vs. experience goods. In P.A. Keller and D.W. rook (eds.), Advances in Consumer Research, vol. 30. Valdosta, GA: Association for Consumer research, 2003, pp. 347–348.

4. Ajzen, I. The theory of planned behavior. Organizational Behavior & Human Decision Processes, 50, 3 (1991), 179–211.


5. Allen, M.; Bruflat, r.; Fucilla, r.; Kramer, M.; McKellips, S.; ryan, D.J.; and Spiegelhoff, M. Testing the persuasiveness of evidence: Combining narrative and statistical forms. Communication Research Reports, 17, 4 (2000), 331–336.

6. Andersen, V.; hansen, C.B.; and Andersen, h.h.K. Evaluation of Agents and Study of End-User Needs and Behaviour for E-Commerce. roskilde, Denmark: riso National labora-tory, 2001.

7. Ansari, A.; Essegaier, S.; and Kohli, r. Internet recommendation systems. Journal of Marketing Research, 37, 3 (2000), 363–375.

8. Archak, N.; Ghose, A.; and Ipeirotis, P.G. Deriving the pricing power of product features by mining consumer reviews. Management Science, 57, 8 (2011), 1485–1509.

9. Ariely, D.; lynch, J.G., Jr.; and Aparicio, M. learning by collaborative and individual-based recommendation agents. Journal of Consumer Psychology, 14, 1–2 (2004), 81–95.

10. Armstrong, J.S., and Overton, T.S. Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14, 3 (1977), 396–402.

11. Awad, N.F., and ragowsky, A. Establishing trust in electronic commerce through online word of mouth: An examination across genders. Journal of Management Information Systems, 24, 4 (Spring 2008), 101–121.

12. Bearden, W.O.; Netemeyer, r.G.; and Mobley, M.F. Handbook of Marketing Scales: Multi-Item Measures for Marketing and Consumer Behavior Research. Newbury Park, CA: Sage, 1993.

13. Benbasat, I., and Barki, h. Quo vadis, TAM? Journal of the Association for Information Systems, 8, 4 (2007), article 16.

14. Benlian, A., and hess, T. The signaling role of IT features in influencing trust and par-ticipation in online communities. International Journal of Electronic Commerce, 15, 4 (2011), 7–56.

15. Berry, M.J.A., and linoff, G.S. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. Indianapolis: John Wiley & Sons, 2004.

16. Bharati, P., and Chaudhury, A. An empirical investigation of decision-making satisfaction in Web-based decision support systems. Decision Support Systems, 37, 2 (2004), 187–197.

17. Bhattacharjee, S.; Gopal, r.D.; lertwachara, K.; and Marsden, J.r. Consumer search and retailer strategies in the presence of online music sharing. Journal of Management Information Systems, 23, 1 (Summer 2006), 129–159.

18. Bickart, B., and Schindler, r.M. Internet forums as influential sources of consumer information. Journal of Interactive Marketing, 15, 3 (2001), 31–40.

19. Brave, S., and Nass, C. Emotion in human–computer interaction. In J. Jacko and A. Sears (eds.), The Human–Computer Interaction Handbook. Mahwah, NJ: lawrence Erlbaum, 2003, pp. 81–96.

20. Britton, B.K.; Glynn, S.M.; Meyer, B.J.; and Penland, M.J. Effects of text structure on use of cognitive capacity during reading. Journal of Educational Psychology, 74, 1 (1982), 51–61.

21. Cheong, h.J., and Morrison, M.A. Consumers’ reliance on product information and rec-ommendations found in UGC. Journal of Interactive Advertising, 8, 2 (2008), 1–29.

22. Cheung, M.y.; luo, C.; Sia, C.l.; and Chen, h. Credibility of electronic word-of-mouth: Informational and normative determinants of on-line consumer recommendations. International Journal of Electronic Commerce, 13, 4 (2009), 9–38.

23. Childers, T.l. Memory for the visual and verbal components of print advertisements. Psychology & Marketing, 3, 3 (1986), 137–149.

24. Chin, W.W. The partial least squares approach for structural equation modelling. In G.A. Marcoulides (ed.), Modern Methods for Business Research. hillsdale, NJ: lawrence Erlbaum, 1998, pp. 295–336.

25. Cugelman, B.; Thelwall, M.; and Dawes, P. The dimensions of Web site credibility and their relation to active trust and behavioural impact. Communications of the Association for Information Systems, 24, 1 (2009), 455–472.

26. Cyr, D.; head, M.; larios, h.; and Bing, P. Exploring human images in Website design: A multi-method approach. MIS Quarterly, 33, 3 (2009), 530–566.

27. Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of informa-tion technology. MIS Quarterly, 13, 3 (1989), 319–339.


28. Davis, F.D.; Bagozzi, r.P.; and Warshaw, P.r. Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22, 14 (1992), 1111–1132.

29. Deighton, J.; romer, D.; and McQueen, J. Using drama to persuade. Journal of Consumer Research, 16, 3 (1989), 335–343.

30. Duhan, D.F.; Johnson, S.D.; Wilcox, J.B.; and harrel, G.D. Influences on consumer use of word-of-mouth recommendation sources. Journal of the Academy of Marketing Science, 25, 4 (1997), 283–295.

31. Eagley, A.h.; Wood, W.; and Chaiken, S. Causal inferences about communicators and their ef-fect on opinion change. Journal of Personality and Social Psychology, 36, 4 (1978), 424–443.

32. Eroglu, S.A.; Machleit, K.A.; and Davis, l.M. Atmospheric qualities of online retailing: A conceptual model and implications. Journal of Business Research, 54, 2 (2001), 177–184.

33. Forman, C.; Ghose, A.; and Wiesenfeld, B. Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information Systems Research, 19, 3 (2008), 291–313.

34. Fornell, C., and larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 1 (1981), 39–50.

35. Gefen, D.; Karahanna, E.; and Straub, D.W. Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27, 1 (2003), 51–90.

36. Gogoi, P. retailers take a tip from MySpace. Bloomberg Businessweek, February 13, 2007 (available at www.businessweek.com/bwdaily/dnflash/content/feb2007/db20070213_626293 .htm).

37. hampton-Sosa, W., and Koufaris, M. The effect of Web site perceptions on initial trust in the owner company. International Journal of Electronic Commerce, 10, 1 (2005), 55–81.

38. hanani, U.; Shapira, A.; and Shoval, P. Information filtering: Overview of issues, research and systems. User Modeling and User-Adapted Interaction, 11, 3 (2001), 203–259.

39. harris, P. Designing and Reporting Experiments in Psychology. New york: Open University Press, 2008.

40. hassanein, K., and head, M. The impact of infusing social presence in the Web interface: An investigation across product types. International Journal of Electronic Commerce, 10, 2 (2005), 31–55.

41. häubl, G., and Murray, K.B. Preference construction and persistence in digital market-places: The role of electronic recommendation agents. Journal of Consumer Psychology, 13, 1–2 (2003), 75.

42. häubl, G., and Trifts, V. Consumer decision making in online shopping environments: The effects of interactive decision aids. Marketing Science, 19, 1 (2000), 4–21.

43. helson, h. Adaption-Level Theory: An Experimental and Systematic Approach to Behavior. New york: harper & row, 1964.

44. hess, T.; Fuller, M.A.; and Campbell, D. Designing interfaces with social presence: Using vividness and extraversion to create social recommendation agents. Journal of the Association for Information Systems, 10, 12 (2009), 889–919.

45. holbrook, M.B., and hirschman, E.C. The experiential aspects of consumption: Consumer fantasies, feelings, and fun. Journal of Consumer Research, 9, 2 (1982), 132–140.

46. hong, W.-C.; Thong, J.y.l.; and Kar yan, T. The effects of information format and shopping task on consumers’ online shopping behavior: A cognitive fit perspective. Journal of Management Information Systems, 21, 3 (Winter 2004–5), 149–184.

47. hovland, C.I., and Weiss, W. The influence of source credibility on communication ef-fectiveness. Public Opinion Quarterly, 15, 4 (1951), 635–650.

48. huang, J.-h., and Chen, y.-F. herding in online product choice. Psychology & Marketing, 23, 5 (2006), 413–428.

49. huang, P.; lurie, N.h.; and Mitra, S. Searching for experience on the Web: An empirical examination of consumer behavior for search and experience goods. Journal of Marketing, 73, 2 (2009), 55–69.

50. Jarvenpaa, S.l. The effect of task demands and graphical format on information processing strategies. Management Science, 35, 3 (1989), 285–303.

51. Jiang, y.; Shang, J.; and liu, y. Maximizing customer satisfaction through an online recommendation system: A novel associative classification model. Decision Support Systems, 48, 3 (2010), 470–479.


52. Kamis, A.; Koufaris, M.; and Stern, T. Using an attribute-based decision support system for user-customized products online: An experimental investigation. MIS Quarterly, 32, 1 (2008), 159–177.

53. King, M.F., and Balasubramanian, S.K. The effects of expertise, end goal, and product type on adoption of preference formation strategy. Journal of the Academy of Marketing Sci-ence, 22, 2 (1994), 146–159.

54. Klein, l.r. Evaluating the potential of interactive media through a new lens: Search versus experience goods. Journal of Business Research, 41, 3 (1998), 195–203.

55. Komiak, S.y.X., and Benbasat, I. The effects of personalization and familiarity on trust and adoption of recommendation agents. MIS Quarterly, 30, 4 (2006), 941–960.

56. Koufaris, M. Applying the technology acceptance model and flow theory to online con-sumer behavior. Information Systems Research, 13, 2 (2002), 205–223.

57. Kumar, N., and Benbasat, I. The influence of recommendations and consumer reviews on evaluations of Websites. Information Systems Research, 17, 4 (2006), 425–439.

58. lowry, P.B.; Vance, A.; Moody, G.; Beckman, B.; and read, A. Explaining and predicting the impact of branding alliances and Web site quality on initial consumer trust of e-commerce Web sites. Journal of Management Information Systems, 24, 4 (Spring 2008), 199–224.

59. Maes, P.; Guttman, r.h.; and Moukas, A.G. Agents that buy and sell. Communications of the ACM, 42, 3 (1999), 81–91.

60. McKnight, D.h.; Choudhury, V.; and Kacmar, C. Developing and validating trust mea-sures for e-commerce: An integrative typology. Information Systems Research, 13, 3 (2002), 334–359.

61. Meyer, r.J. The learning of multiattribute judgment policies. Journal of Consumer Re-search, 14, 2 (1987), 155–173.

62. Montaner, M.; lópez, B.; and lluís de la rosa, J. A taxonomy of recommender agents on the Internet. Artificial Intelligence Review, 19, 4 (2003), 285–330.

63. Mudambi, S.M., and Schuff, D. What makes a helpful online review? A study of customer reviews on Amazon.com. MIS Quarterly, 34, 1 (2010), 185–200.

64. Nelson, P. Information and consumer behavior. Journal of Political Economy, 78, 2 (1970), 311–329.

65. Nelson, P. Advertising as information. Journal of Political Economy, 82, 4 (1974), 729–754.

66. Neufeld, D.; Dong, l.; and higgins, C. Charismatic leadership and user acceptance of information technology. European Journal of Information Systems, 16, 4 (2007), 494–510.

67. O’Keefe, D.J. Persuasion: Theory and Research. Thousand Oaks, CA: Sage, 2002.68. Parboteeah, D.V.; Valacich, J.S.; and Wells, J.D. The influence of Website characteristics on

a consumer’s urge to buy impulsively. Information Systems Research, 20, 1 (2009), 60–78.69. Park, D.-h., and lee, J. eWOM overload and its effect on consumer behavioral intention

depending on consumer involvement. Electronic Commerce Research and Applications, 7, 4 (2008), 386–398.

70. Park, D.-h.; lee, J.; and han, I. The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement. International Journal of Electronic Commerce, 11, 4 (2007), 125–148.

71. Park, J.; Gu, B.; and Konana, P. Impact of multiple word of mouth sources on retail sales. In Proceedings of the Thirtieth International Conference on Information Systems. Atlanta: As-sociation for Information Systems, 2009, pp. 1–14.

72. Pathak, B.; Garfinkel, r.; Gopal, r.D.; Venkatesan, r.; and yin, F. Empirical analysis of the impact of recommender systems on sales. Journal of Management Information Systems, 27, 2 (Fall 2010), 159–188.

73. Pavlou, P.A. Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7, 3 (2003), 101–134.

74. Payne, J.W., and Bettman, J.r. Behavioral decision research: A constructive processing perspective. Annual Review of Psychology, 43 (1993), 87–131.

75. Podsakoff, P.M.; MacKenzie, S.B.; lee, J.; and Podsakoff, N.P. Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 5 (2003), 879–903.


76. Qiu, l., and Benbasat, I. Evaluating anthropomorphic product recommendation agents: A social relationship perspective to designing information systems. Journal of Management Infor-mation Systems, 25, 4 (Spring 2009), 145–181.

77. reeves, B., and Nass, C. The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places. New york: Cambridge University Press, 1996.

78. reinard, J.C. The empirical study of the persuasive effects of evidence: The status after fifty years of research. Human Communication Research, 15, 1 (1988), 3–59.

79. richardson, h.A.; Simmering, M.J.; and Sturman, M.C. A tale of three perspectives: Ex-amining post hoc statistical techniques for detection and correction of common method variance. Organizational Research Methods, 12, 4 (2009), 762–800.

80. riesenbeck, h., and Perrey, J. Power Brands: Measuring, Making, and Managing Brand Success. Weinheim, Germany: Wiley, 2009.

81. ringle, C.M.; Wende, S.; and Will, A. SmartPlS 2.0 (M3) beta. University of hamburg, hamburg, Germany, 2005.

82. russell, J.A. Core affect and the psychological construction of emotion. Psychological Review, 110, 1 (2003), 145–172.

83. russell, J.A. Emotion, core affect, and psychological construction. Cognition & Emotion, 23, 7 (2009), 1259–1283.

84. Sadoski, M.; Goetz, E.T.; and Kangiser, S. Imagination in story response: relationships between imagery, affect, and structural importance. Reading Research Quarterly, 23, 3 (1988), 320–336.

85. Sanchez-Franco, M.J. WebCT—The quasimoderating effect of perceived affective quality on an extending technology acceptance model. Computers & Education, 54, 1 (2010), 37–46.

86. Schlosser, A.E.; Mick, D.G.; and Deighton, J. Experiencing products in the virtual world: The role of goal and imagery in influencing attitudes versus purchase intentions. Journal of Con-sumer Research, 30, 2 (2003), 184–198.

87. Senecal, S., and Nantel, J. The influence of online product recommendation on consumers’ online choices. Journal of Retailing, 80, 2 (2004), 159–169.

88. Simon, h.A. A behavioral model of rational choice. Quarterly Journal of Economics, 69, 1 (1955), 99–118.

89. Smith, D.; Menon, S.; and Sivakumar, K. Online peer and editorial recommendations, trust, and choice in virtual markets. Journal of Interactive Marketing, 19, 3 (2005), 15–37.

90. Sobel, M.E. Asymptotic confidence intervals for indirect effects in structural equation models. In S. leinhardt (ed.), Sociological Methodology. San Francisco: Jossey-Bass, 1982, pp. 290–312.

91. Swaminathan, V. The impact of recommendation agents on consumer evaluation and choice: The moderating role of category risk, product complexity, and consumer knowledge. Journal of Consumer Psychology, 13, 1–2 (2003), 93–101.

92. Tal-Or, N.; Boninger, D.S.; Poran, A.; and Gleicher, F. Counterfactual thinking as a mecha-nism in narrative persuasion. Human Communication Research, 30, 3 (2004), 301–328.

93. Turel, O.; yuan, y.; and Connelly, C.E. In justice we trust: Predicting user acceptance of e-cus-tomer services. Journal of Management Information Systems, 24, 4 (Spring 2008), 123–151.

94. van der heijden, h. User acceptance of hedonic information systems. MIS Quarterly, 28, 4 (2004), 695–704.

95. Vessey, I. Cognitive fit: A theory-based analysis of the graphs versus tables literature. Deci-sion Sciences, 22, 2 (1991), 219–240.

96. von Abrams, K. Germany online: Europe’s biggest e-commerce market comes of age. Consulting report, eMarketer, May 2009 (available at www.emarketer.com/report .aspx?code=emarketer_2000595/).

97. Wang, W., and Benbasat, I. Trust in and adoption of online recommendation agents. Journal of the Association for Information Systems, 6, 3 (2005), 72–101.

98. Wang, W., and Benbasat, I. recommendation agents for electronic commerce: Effects of explanation facilities on trusting beliefs. Journal of Management Information Systems, 23, 4 (Spring 2007), 217–246.

99. Wathen, C.N., and Burkell, J. Believe it or not: Factors influencing credibility on the Web. Journal of the American Society for Information Science and Technology, 53, 2 (2002), 134–144.


100. Weathers, D.; Sharma, S.; and Wood, S.l. Effects of online communication practices on consumer perceptions of performance uncertainty for search and experience goods. Journal of Retailing, 83, 4 (2007), 393–401.

101. Wei, y.Z.; Moreau, l.; and Jennings, N.r. A market-based approach to recommender systems. ACM Transactions on Information Systems, 23, 3 (2005), 227–266.

102. Whetten, D.A. What constitutes a theoretical contribution? Academy of Management Review, 14, 4 (1989), 490–495.

103. Xia, l., and Bechwati, N.N. Word of mouse: The role of cognitive personalization in online consumer reviews. Journal of Interactive Advertising, 9, 1 (2008), 3–13.

104. Xiao, B., and Benbasat, I. E-commerce product recommendation agents: Use, charac-teristics, and impact. MIS Quarterly, 31, 1 (2007), 137–209.

105. Zeng, C.; Xing, C.-X.; Zhou, l.-Z.; and Zheng, X.-h. Similarity measure and instance selection for collaborative filtering. International Journal of Electronic Commerce, 8, 4 (2004), 115–129.

106. Zhang, P. Toward a positive design theory: Principles for designing motivating informa-tion and communication technologies. In M. Avital, r.J. Boland, and D.l. Cooperrider (eds.), Designing Information and Organizations with a Positive Lens, vol. 2. Amsterdam: JAI Press, 2008, pp. 45–74.

107. Zhang, P., and li, N. love at first sight or sustained effect? The role of perceived affective quality on users’ cognitive reactions to information technology. In Proceedings of the Twenty-Fifth International Conference on Information Systems. Atlanta: Association for Information Systems, 2004, pp. 283–295.

108. Zhang, P., and li, N. The importance of affective quality. Communications of the ACM, 48, 9 (2005), 105–108.

109. Zwass, V. Co-creation: Toward a taxonomy and an integrated research perspective. International Journal of Electronic Commerce, 15, 1 (2010), 11–48.

Copyright of Journal of Management Information Systems is the property of M.E. Sharpe Inc. and its content

may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express

written permission. However, users may print, download, or email articles for individual use.

Differential Effects of Provider Recommendations and Consumer Reviews in E-Commerce Transactions. an...

Documents

Transcript of Differential Effects of Provider Recommendations and Consumer Reviews in E-Commerce Transactions. an...