Effects of Recommendation Neutrality and Sponsorship Disclosure on Trust vs. Distrust in Online Recommendation Agents: Moderating Role of Explanations for Organic Recommendations

Product recommendation agents (RAs) are widely employed by online merchants to facilitate consumers’ decision making. Users’ perceived integrity of these RAs becomes a critical trust concern when RAs apply sponsorship practices and recommend products biased toward sponsored products. Sponsorship disclosure is enforced by the U.S. Federal Trade Commission, but many technologies fail to comply probably because of their concerns on users’ trust in the biased technologies. This research investigate when sponsorship disclosure is most effective in enhancing users’ perceived RA integrity. A laboratory experiment revealed two major findings related to the benefits of sponsorship disclosure in enhancing users’ perceived integrity of a biased RA. First, for users with high prior knowledge about the prevalence of sponsorships used by RAs in general, sponsorship disclosure can reduce users’ perceived psychological contract violations of a biased RA and then increase users’ perceived RA integrity. For users with limited such prior knowledge, the disclosure fails to reduce these perceived violations. Second, regardless of the level of such prior knowledge of users, sponsorship disclosure enhances users’ perceived transparency of a biased RA, which, in turn, leads to perceived RA integrity.

Due to advances in Web-based technologies, ample opportunities exist to utilize knowledge-based systems for facilitating online consumer decision-making and for providing recommendation services for consumers. This thesis focuses on online recommendation agents that offer shopping advice based on user-specified needs and preferences. Because of the high risks and uncertainties inherent in online environments, effective recommendation agents need to be trustworthy. By extending interpersonal trust to trust in technological artifacts, consumers' trust in a recommendation agent is defined to include three belief components: competence, benevolence, and integrity. This thesis examines user acceptance of online recommendation agents and trust formation in the agents and it empirically investigates agent features and capabilities that increase the trust in them so that a higher chance of user acceptance can be realized. Two important agent capabilities are tested: (1) explanation facilities; and (2) decision strategy support. An integrated Trust-TAM (Technology Acceptance Model) was tested and the results show that trust in agents influences consumers' behavioral intentions. Trust in agents exerts a direct impact on the intentions to adopt recommendation agents as well as an indirect impact via the perceived usefulness of the agents. Written protocols were collected and analyzed to identify the major processes that build and inhibit consumers' trust in recommendation agents. The results highlight the important roles of several processes in cultivating and inhibiting agent trust, such as expectation confirmation, utility assessment, and information sharing. Regarding explanation facilities, this research tests three types of explanations---how explanations, why explanations, and guidance. The results indicate that the use of different types of explanations increases different trusting beliefs: the use of how explanations increases competence and benevolence beliefs; the use of why explanations increases the benevolence belief; and the use of guidance increases the integrity belief. The impact of decision strategy support on consumers' trust and adoption of online recommendation agents was also investigated together with explanation facilities. Three types of recommendation agents with different levels of decision strategy support were compared. Both the benefits and costs of providing a high level of decision strategy support were examined. The results suggest that recommendation agents with decision strategy support capabilities and explanation facilities deliver benefits to users (e.g., more useful and trustworthy) and have a higher chance of being adopted by users, when the use of the agents does not require much additional effort. This research has addressed an important gap that exists in our current understanding of trustworthy online recommendation agents. It also makes a key contribution by empirically testing the effects of explanation facilities and decision strategy support on consumers' trust and acceptance of online recommendation agents.

Online product recommendation agents are becoming increasingly prevalent on a wide range of websites. These agents assist customers in reducing information overload, providing advice to find suitable products, and facilitating online decision-making. Consumer trust in recommendation agents is an integral factor influencing their successful adoption. However, the nature of trust in technological artifacts is still an under-investigated and not well understood topic. Online recommendation agents work on behalf of individual users (principals) by reflecting their specific needs and preferences. Trust issues associated with online recommendation agents are complicated. Users may be concerned about the competence of an agent to satisfy their needs as well as its integrity and benevolence in regard to acting on their behalf rather than on behalf of a web merchant or a manufacture. This study extends the interpersonal trust construct to trust in online recommendation agents and examines the nomological validity of trust in agents by testing an integrated Trust-TAM (Technology Acceptance Model). The results from a laboratory experiment confirm the nomological validity of trust in online recommendation agents. Consumers treat online recommendation agents as “social actors” and perceive human characteristics (e.g., benevolence and integrity) in computerized agents. Furthermore, the results confirm the validity of Trust-TAM to explain online recommendation acceptance and reveal the relative importance of consumers’ initial trust vis-a-vis other antecedents addressed by

We empirically test the effects of explanation facilities on consumers' initial trusting beliefs concerning online recommendation agents (RAs). RAs provide online shopping advice based on user-specified needs and preferences. The characteristics of RAs that may hamper consumers' trust building in the RAs are identified, and the provision of explanation facilities is proposed as a knowledge-based approach to enhance consumers' trusting beliefs by dealing with these obstacles. This study examines the effects of three types of explanations about an RA and its use—how, why, and trade-off explanations—on consumers' trusting beliefs in an RA's competence, benevolence, and integrity. An RA was built as the experimental platform and a laboratory experiment was conducted. The results confirm the important role of explanation facilities in enhancing consumers' initial trusting beliefs and indicate that consumers' use of different types of explanations enhances different trusting beliefs: the use of how explanations increases their competence and benevolence beliefs, the use of why explanations increases their benevolence beliefs, and the use of trade-off explanations increases their integrity beliefs.

competence, integrity, and benevolence are the three key trusting beliefs that are widely acknowledged in the trust literature. Drawing on users’ different dispositional attribution of these trusting beliefs, we investigate the different influence of two sets of experiential reasons on the competence belief versus the benevolence and integrity beliefs in online recommendation agents (RAs). The two sets of experiential reasons encompass interactive reason, including three performance factors (namely, perceived cognitive effort, advice quality, and perceived strategy restrictiveness), and knowledge-based reason (i.e., perceived transparency of an RA). Data were collected through a laboratory experiment to test our hypotheses. Results demonstrate that the three performance factors affect only the competence belief, whereas perceived RA transparency influences all three trusting beliefs. In addition, the effects of perceived transparency on competence are partially mediated by perceived cognitive effort and advice quality. The research contributes to the trust literature by revealing the different antecedents of the three trusting beliefs and provides guidelines for designers to choose specific design elements to improve a particular trusting belief of the user toward an RA.

As organizations increasingly utilize Web-based technologies to support customers better, trust in decision support technologies has emerged as an important issue in online environments. In this study, we identify six reasons users trust (or do not trust) a technology in the early stages of its use by extending the theories of trust formation in interpersonal and organizational contexts to that of decision support technologies. We study the particular context of decision support technologies for e-commerce: online recommendation agents (RAs), which facilitate users' decision making by providing advice on what to buy based on user-specified needs and preferences. A laboratory experiment is conducted using a multimethod approach to collect data. Both quantitative data about participants' trust in RAs and written protocols that explain the reasons for their levels of trust are collected. A content analysis of the written protocols identifies both positive and negative trust attributions that are then mapped to six trust reasons. A structural equation modeling analysis is employed to test the causal strengths of the trust reasons in explaining participants' trust in RAs. The results reveal that in the early stages of trust formation, four positive reasons (i.e., knowledge-based, interactive, calculative, and dispositional) are associated with higher trust in RAs and two negative reasons (i.e., calculative and interactive) are associated with lower trust in RAs. The results also demonstrate some distinctive features of trust formation with respect to decision support technologies. We discuss the research and practical implications of the findings and describe opportunities for future research.

ABSTRACTIn this study, we leverage valence theory, cognitive absorption theory, and IT adoption literature to investigate the perceptions of consumers towards the use of online Recommendation Agents (RAs) that vary in the number of details they provide in eliciting consumers’ preferences and presenting recommendations accordingly. The research model is empirically validated via an experiment involving 197 online shoppers. Results show that high in-depth RAs are better alternatives to low in-depth RAs in driving consumers’ intention to use RAs in their shopping experience. The findings provide novel insights for researchers and practitioners interested in understanding the proper design for online RAs.

PurposeThe purpose of this study is to deploy the psychological lens of “expectancy violation” to examine the effects of social media influencers’ (SMIs) sponsorship disclosure on social media users’ (SMUs) behavioral outcomes (i.e. influencer avoidance, influencer switching and brand avoidance) and whether these relationships are moderated by SMIs’ honesty declaration and SMU cynicism.Design/methodology/approachA 2x2 between-subjects experimental design was used across four studies. Data collected across four online experiments were analyzed.FindingsStudy 1 found that sponsorship disclosures increased influencer avoidance, influencer switching and brand avoidance. Study 2 found that SMUs’ psychological contract violation with SMIs mediated these relationships. However, SMIs’ effective honesty declaration statements (vs no declaration) subdued SMUs’ negative behavioral outcomes. Study 3 elucidated that SMUs’ cynicism (vs no cynicism) accentuated the effects of sponsorship disclosures on influencer avoidance, influencer switching and brand avoidance. Studies 2 and 3 supported moderated mediation effect through SMUs’ psychological contract violation for honesty declaration but not SMU cynicism.Practical implicationsThis study elucidates SMUs’ evaluation of brand-sponsored SMI posts and provides managers with tools such as honesty declaration statements and tags to offset the negative effects on consumer behavioral outcomes.Originality/valueThis is one of the initial studies investigating SMUs’ psychological contract violation and the effects of SMUs’ cynicism in SMIs’ sponsorship disclosure context. Also, this study conceptualizes a novel construct, influencer switching, as one of the consequences of sponsorship disclosure.

Many retailers (e.g., Amazon, Walmart) use various types of online recommendation agents (RAs) on their websites to suggest goods and services to consumers. These RAs screen millions of options to ease consumers’ information search and evaluation. To determine which RA types best support consumers’ efforts, the present research reports a meta-analysis of perceived recommendation quality research, a key performance metric that gauges RAs from consumers’ perspectives. To test the framework derived from this meta-analysis, the authors rely on data gathered from 32,172 consumers, reported in 122 samples. The results affirm that some RAs perform better than others in leveraging the effects of perceived recommendation quality on consumers’ decision-making satisfaction, RA satisfaction, and intention to use the RA in the future. The best performing RAs feature specific algorithms (i.e., collaborative filtering, interactive RAs, and self-serving recommendations), recommendation presentations (i.e., solicited recommendation), and data sources (i.e., location-based and social network–based RAs). Moreover, the results suggest that some RAs perform better than others in leveraging the effects of decision-making and RA satisfaction on future use intentions. These insights advance RA theory and provide guidance for managers, with regard to choosing the optimal RA.

Online product recommendation agents (RAs) are gaining greater strategic importance as a critical touch-point between marketers and consumers. Yet, the role of consumer participation in using RAs has not been examined. This study shows that greater consumer participation in using an RA leads to more satisfaction, greater trust, and higher purchase intentions, related to the RA and its recommendations. In contrast, the financial risk (associated with the product under consideration) reduces satisfaction, trust, and purchase intentions, and it also moderates the effect of consumer participation on these same variables. The findings extend the literature and suggest actionable implications for marketing strategy.

PurposeThis paper investigates the effects of advising strength of a recommendation agent on users' trust and distrust beliefs and how the effects are moderated by perceived brand familiarity.Design/methodology/approachA research model is evaluated using a laboratory experiment with 149 participants.FindingsResults reveal that a strong advising tone leads to higher trust in terms of users' credibility and benevolence beliefs and lower distrust in terms of their discredibility beliefs (the trustor's concerns regarding the trustee's dishonesty and competence in engaging in harmful behavior) when perceived brand familiarity is high. By contrast, when brand familiarity is low, strong advising tone results in low trust in terms of users' credibility belief and high distrust in terms of their beliefs in discredibility and malevolence (concerns regarding the trustee's conduct in terms of a malicious intention that can hurt the trustor's welfare).Originality/valueThis paper contributes to the trust and distrust literature by studying how each of the dimensions of trust and distrust can be affected by an RA's design feature. It extends the attribution theory to the RA context by studying the moderating role of brand familiarity in determining the effects of the advising strength of an RA. It provides actionable guidelines for practitioners regarding the adoption of an RA's appropriate advising strength to promote different types of products.

Enjoying local food could be one of the motives for tourism, and local food and restaurant recommendation information would be important for tourists to decide their destination. Recommendation agents are the sorting and searching function to find the best local food and restaurant among the complexity of information, and they could also be helpful for the tourist to decide their destination. Online tourism websites (e.g., Ctrip.com ) started to provide restaurant recommendations containing food-related information and recommendation agents to attract tourists. However, few studies have investigated their impact on the destination visit intention of potential Chinese tourists. This study aims to empirically validate how restaurant recommendation information, including food-related information and recommendation agents, could impact online tourists’ reactions, such as satisfaction, continuous website usage, and destination visits. We developed our hypothesis based on the information system (IS) success model. We gathered 202 data points from potential tourists using quasi-experimental methods, and these data were analyzed by the PLS algorithm. The results indicate that restaurant recommendation information and recommendation agents significantly increase the perceived information quality and perceived system quality. Increased perceived information quality and system quality could significantly increase potential tourists’ satisfaction, website continuous usage intention, and destination visit intention. The results of this study could contribute to making tourism websites more attractive by using local food and restaurant information and recommendation agents.

Recommendation agents (RAs) are software agents that elicit the interests or preferences of individual consumers for products, either explicitly or implicitly, and make recommendations accordingly. RAs have the potential to support and improve the quality of the decisions consumers make when searching for and selecting products online. They can reduce the information overload facing consumers, as well as the complexity of online searches. Prior research on RAs has focused mostly on developing and evaluating different underlying algorithms that generate recommendations. This paper instead identifies other important aspects of RAs, namely RA use, RA characteristics, provider credi'r, and user-RA interaction, which influence users' decision-making processes and outcomes, as well as their evaluation of RAs. It goes beyond generalized models, such as TAM, and identifies the RA-specific features, such as RA input, process, and output design characteristics, that affect users' evaluations, including their assessments of the usefulness and ease-of-use of RA applications. Based on a review of existing literature on e-commerce RAs, this paper develops a conceptual model with 28 propositions derived from five theoretical perspectives. The propositions help answer the two research questions: (1) How do RA use, RA characteristics, and other factors influence consumer decision making processes and outcomes? (2) How do RA use, RA characteristics, and other factors influence users' evaluations of RAs? By identifying the critical gaps between what we know and what we need to know, this paper identifies potential areas of future research for scholars. It also provides advice to information systems practitioners concerning the effective design and development of RAs.

The purpose of this paper is to report the results of a laboratory experiment that investigated how assortment planners’ perceptions, usage behavior, and decision quality are influenced by the way recommendations of an artificial intelligence (AI)-based recommendation agent (RA) are presented. A within-subject laboratory experiment was conducted with twenty subjects. Participants perceptions and usage behavior toward an RA while making decisions were assessed using validated measurement scales and eye-tracking technology. The results of this study show the importance of a transparent RA demanding less cognitive effort to understand and access the explanations of a transparent RA on assortment planners’ perceptions (i.e., source credibility, sense of control, decision quality, and satisfaction), usage behavior, and decision quality. Results from this study suggest that designing RAs with more transparency for the users bring perceptual and attitudinal benefits that influence both the adoption and continuous use of those systems by employees. This study contributes to filling the literature gap on RAs in organizational contexts, thus advancing knowledge in the human–computer interaction literature. The findings of this study provide guidelines for RA developers and user experience (UX) designers on how to best create and present an AI-based RA to employees.

Artificial intelligence (AI) based recommendation agents (RA) can help managers make better decisions by processing a large quantity of decision relevant information. Research on user-RA interactions show that users benefit from RA, but that there are some challenges to their adoption. For instance, RA adoption can only happen if users trust the RA. Thus, this study investigates how the richness of the information provided by an RA and the effort necessary to reach this information influence users' perceptions and usage. A within-subject lab experiment was conducted with 20 participants. Results suggest that perceptions toward the RA (trust, credibility, and satisfaction) are influenced by the RA information richness, but not by the effort needed to reach this information. In addition to contributing to HCI literature, the findings have implications for the design of better AI-based RA systems.