Creating More Credible and Likable Travel Recommender Systems

Transcription

1 Creating More Credible and Likable Travel Recommender Systems Kyung Hyan Yoo Laboratory for Intelligent Systems in Tourism Department of Recreation, Park & Tourism Sciences Texas A&M University, USA Progress: In the beginning of proposal writing stage Abstract Travel recommender systems are taking on an important role in helping online users make decisions. However, system users may not automatically take the recommendations into account for their decision-making process. It is important to understand what factors can influence users to accept the recommendations provided by recommender systems. Perceived credibility and liking of the recommender system are proposed as important factors that influence user s likelihood to accept recommendations. The paper outlines a study which will manipulate similarity and authority cues embedded in recommender systems and will test whether they influence perceptions of credibility and liking. Keywords: recommender systems; credibility; liking; similarity; authority. 1 Introduction Recently, recommender systems are taking on an important role in helping online users with their capacity of providing personalized and customized recommendations (Kim & Kim, 2001). Despite their promise as decision aids and their capacity to provide personalized recommendations, system users might not automatically take the recommendations into account for their decision-making process. It is important to understand what factors can influence users to accept the recommendations provided by recommender systems. The persuasion literature suggests that people more likely accept recommendations from credible and likable sources (O Keefe, 2002). Fogg (2002, 2003) recently found that source credibility also matters when computers take the advisory role. However, little is known about what characteristics of recommender systems can influence users perceptions of system credibility and liking. Importantly, Aksoy et al. (2006) found similarity between recommender systems and users increases users' preference to use the system. In addition, Fogg (2003) suggested that adopting roles of authority will enhance computing technology's credibility and power of persuasion. Consequently, this study will test whether similarity and authority of recommender systems can influence users perceptions of system credibility and liking.

2 2 Theoretical Background 2.1 Perceived Credibility and Liking of Recommendation Source Credibility. Credibility can be defined as judgments made by a perceiver concerning the believability of a communicator (Fogg, Lee and Marshall, 2002; O Keefe, 2002). Although the literature suggests various dimensions of credibility, most researchers agree that it is comprised of two key elements: trustworthiness and expertise (Fogg et al, 2002; Fogg 2003; O Keefe, 2002; Rhoads & Cialdini, 2002). A great deal of source credibility studies have found a more credible source was more preferred and persuasive (Kelman, 1961; Anderson & Clevenger, 1963; McGuire, 1968). Recent studies additionally confirmed that source credibility is positively correlated with message recipients attitude and behavioral intentions and behaviors (Lascu, et al., 1995; Senecal & Nantel, 2004). Importantly, Fogg (2002, 2003) found that credibility also matters for technology, in particular when computers give advice. Liking. People mindlessly tend to agree with those who are seen as likable (Burgoon et al., 2002). Liking refers to the affective bond that an individual may feel toward another person (Smith et al., 2005). Research generally supports the assumption that liked communicators are more effective influence agents than are disliked communicators (Eagly & Chaiken, 1975; Giffen & Ehrlich, 1963; Sampson & Insko, 1964) and likability has been labeled a persuasion tactic and a scheme of selfpresentation (Cialdini, 1993; Kenrick et al., 2002). O Keefe (2002) stressed enhanced liking for the source is commonly accompanied by enhanced judgments of the communicator s trustworthiness and a number of studies found that similarity to ourselves increases likability (Byrne, 1971; Carli et al., 1991; Hogg et al., 1993). 2.2 Authority and Similarity as Determinants of Credibility and Liking Authority. Many researchers indicate that we often embrace the mental shortcut of assuming that people who simply display symbols of authority such as titles, tailors and tone should be listened to (Fogg, 2003; Rhoads & Cialdini, 2002). Authority is clearly linked to credibility and persuasiveness but more research is needed to explore more fully the relationships among authority, credibility and persuasion. Recently, Fogg (2003) argued that websites displaying awards or third-party endorsements such as seals of approval can enhance their credibility. Similarity. According to Heider s (1958) balance theory people tend to like others who exhibit signs of similarity because it is reinforcing to their own self-concept and helps them to predict and understand similar others. Michener et al. (2004) also explained similarity as the determinants of liking. In the context of technology, Nass and Moon (2000) examined the impact of similarity on the computer user s credibility perception of computers and found that those who matched (in terms of personality) with the computer evaluated the computer s credibility higher than those who were dissimilar. Importantly, Aksoy et al. (2006) studied how similarity influences recommender system users intention to use and found that similarity is an important factor that makes users prefer to use a system.

3 3 Conceptual Development and Hypothesis Based on the existing literature, a conceptual model was developed (Fig. 1). A total of six hypotheses were derived from this model and will be tested in the proposed study. Perceived Credibility H 1a Expertise Authority H 2a H 1b H 2b Trustworthiness Similarity H 2c H 3 Perceived Liking Fig. 1. Conceptual model of authority and similarity impacts on perceptions of recommendation systems H 1a Authority cues positively influence perceptions of recommender system expertise. H 1b Authority cues positively influence perceptions of recommender system trustworthiness. H 2a Similarity cues positively influence perceptions of recommender system expertise. H 2b Similarity cues positively influence perceptions of recommender system trustworthiness. H 2c Similarity cues positively influence recommender system liking. H 3 Recommender system liking positively influences perceptions of recommender system trustworthiness. 4 Proposed Methodology A Web-based experiment will be conducted to test the hypotheses in the context of a travel recommender system. 4.1 Manipulations In this experiment, two factors will be manipulated with two levels per factor, thus leading to a 2 x 2 full-factorial between-subjects design. The first factor is authority (high, low) and the second factor is similarity in terms of age (similarity/young,

4 dissimilarity/old). A number of pre-tests will be conducted to identify successful manipulations. Authority. In the high authority condition, the recommender system interface will include a sentence, Your Travel Plan Expert. This manipulation is consistent with the theoretical definition of authority as status, dominance and power, as well as expertise (Levine, 2003; Burgoon et al., 2002). Conversely, in the low authority condition, Your travel plan system will be presented. Manipulation will be checked with a 7 point Likert scale that asks the participants to rate if the recommender system is authoritative. Similarity. In the similarity condition, the recommender system interface will be designed using a text font more likely perceived as young such as Comic Sans MS while the dissimilarity condition interface will be designed with a font more likely perceived as old such as Times New Roman or Monotype Corsiva. Manipulation will be checked with a 7 point Likert scale that asks the participants to rate if the recommender system is similar to them. 4.2 Participants & Procedure A total of 100 undergraduate students will be recruited in order to obtain a minimum of 25 responses per condition. The students will be solicited for the experiment through classes offered at Texas A&M University with partial course credits as an incentive. After arriving at the laboratory, participants will be greeted and seated in front of a computer. The computer will show an introductory screen explaining the goal of the study. Participants will be informed that a travel agency is interested in embedding a newly developed recommender system on its website and would like to evaluate the systems. Then, participants are given a hypothetical situation which asks them to search for destinations for a 5 day pleasure trip during a winter vacation. Upon leaving the scenario screen, participants will be randomly assigned to one of the experimental conditions, showing the homepage of a travel recommender system. Participants will then be asked to evaluate the system in terms of credibility and liking. To measure the user s perceived credibility and liking for recommender systems, credibility scales will be adapted from Yoo and Gretzel (2006) and liking measurement scales for recommender systems will be developed and tested. 5 Anticipated Results It is expected that recommender system users perceived credibility of the system will be increased when the system presents high authoritative characteristics and also it is expected that the similarity of the travel recommender system will positively influence users perceptions of recommender system credibility and liking. In addition, participants greater perceived liking is expected to result in greater perceived trustworthiness of the recommender system.

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