“Do you trust me?” – A Structured Evaluation of Trust and Social Recommendation Agents

Recommender systems are considered as useful software that helps users in screening and evaluating products. The fact that users do not know how these systems make decisions leads to an information asymmetry. Thus, users need to trust if they want to take over systems’ recommendations. Applying social interfaces has been suggested as helpful extensions of recommender systems to increase trust. These are called (Social) Recommendation Agents. While many articles and implementations can be found in the field of e-commerce, we believe that Recommendation Agents can be applied to other contexts, too. However, a structured evaluation of contexts and design dimensions for Recommendation Agents is lacking. In this study, first, we give an overview of design dimensions for Recommendation Agents. Second, we explore previous research on trust and Recommendation Agents by means of a structured literature review. Finally, based on the resulting overview, we highlight three major areas for future research

Psychological Contract Violation in Recommendation Agent Use

We examine whether psychological contract theory can explain users’ responses to e-commerce recommendation agents (RAs). Theories of social response to technology, trust in technology, and technology adoption are used to adapt psychological contract theory from the interpersonal domain to user-RA domain. We theorize that a psychological contract breach will cause a negative emotional reaction, called a psychological contract violation, which, via trust and usefulness perceptions, will influence users’ intentions to follow an RAs’ recommendation. Two studies elicited perceived user-RA mutual obligations, which form the basis for the posited psychological contract. We outline a Study 3 to measure preference strength for these obligations, and a Study 4 to test the effect of breaching these obligations on theorized emotional, cognitive, and behavioral reactions to the RA. Using these studies, insights can be gained about how to design RAs to achieve important business results and avoid negative side effects

Investigating the Effect of Persuasive Design on Online Users’ Persuasion Awareness

Due to the growth of Big Data and associated technologies, persuasion practices in online settings have been increasing. However, the use of technologies is a two-edged sword. Technologies can be used to influence users without their awareness of being persuaded, making them more vulnerable to such influence. Recently this concern has been more pronounced due to the revelation of Facebook’s target ads sponsored by the Russian government during the 2016 US presidential election. Despite its importance, persuasion awareness has received less attention in IS research. As technologies have been embedded throughout online platforms and provided more insights about their users, there is a substantial possibility persuading users via the use of technology design. Thus, the likelihood of being persuaded without awareness will increase. To this end, we aim to address the two specific research questions: What are key forms of persuasive design which influence online users’ persuasion awareness? How does persuasive design form influence online users’ persuasion awareness and behavioral responses? To answer these questions, we apply Persuasion Knowledge Model which explains persuasion awareness in the offline context and personalization literature to outline how online users perceive and respond to a persuasion attempt triggered by persuasive website design. Drawing on Decision Support System, we identify three forms of persuasive design in online settings—suggestive, informative, and supportive design. We expect that this research will provide a theoretical model to enhance understanding of online users’ persuasion awareness and inform website designers to design websites which promote users’ informed judgments and decisions

THE INFLUENCE OF SOCIAL PRESENCE ON EVALUATING PERSONALIZED RECOMMENDER SYSTEMS

Providing recommendations is acknowledged as an important feature of a business-to-consumer online storefront. Although many studies have been conducted the algorithms and operational procedures relating to personalized recommender systems, empirical evidence demonstrating relationships between social presence and two important outcomes of evaluating recommender systems, reuse intention and trust, remains lacking. To test the existence of a causal link between social presence and reuse intention, and the mediating role of trust between these two variables, this study conducted experiments varying the levels of social presence while providing personalized recommendations to users based on their explicit preferences. This study also compared these effects in two different product contexts: hedonic and utilitarian products. Interactions of social presence and customer reviews were also investigated in these experiments. The results show that higher social presence increases both reuse intention and trust in recommender systems. In addition, the influence of social presence on reuse intention in the context of recommending utilitarian products is less than that in the context of recommending hedonic products

Trustworthy Virtual Advisors and Enjoyable Interactions: Designing for Expressiveness and Transparency

Online virtual advisors have enjoyed an increased research attention and widespread use in the lastseveral years. In investigating the determinants of their adoption, the majority of extant research hasfocused on a set of utilitarian variables that address some outcomes from their use. In contrast, thisstudy focuses on users’ perceptions of these virtual advisors as interaction partners, and on beliefsusers form during these interactions. Specifically, we propose and test for the effects of perceivedadvisor expressiveness and transparency on perceptions of their trustworthiness and interactionenjoyment. The latter two constructs are further proposed to act as antecedents to users’ reuseintentions. The results of an experimental study lend support to the proposed model, and highlight theimportance of designing social and trustworthy advisors and enjoyable interactions

EXPLORING INTENTION TO REUSE RECOMMENDATION AGENTS FROM ACCESSIBILITY-DIAGNOSTICITY PERSPECTIVE: THE MODERATING EFFECT OF DOMAIN KNOWLEDGE

Recommendation agents help users reduce information overload and improve decision quality. Yet, many online shoppers have negative reaction or have no motivation to use recommendation agents, since they have no idea of whether users can achieve their shopping goals with less effort. We think information is fundamental to using recommendation agents. This study develops a research framework from the accessibility-diagnosticity perspective and proposes explanation facility, perceived similarity and information diagnosticity are important determinants of users’ intention to reuse RAs. We think explanation facility could persuade users of RAs’ performance, similarity could move users to agree with RAs, and information diagnosticity could let users be capable of evaluating RAs. We also consider the moderating role of domain knowledge on relationship of similarity and information diagnosticity. This study conducted a 2*2 factorial experiment for data collection. Results show that decision process and outcome similarity indirectly influence reuse intention by information diagnosticity and the effects of process and outcome similarity varies with degrees of users’ domain knowledge. The influence of explanation facility on similarity is not obvious. The effect of “why” explanation facility on outcome explanation is significantly contrary to our expectation. Explanation facility may have to be utilized carefully. Implications are discussed

Towards Designing an Assistant for Semi-Automatic EMS Dispatching

Many Emergency Medical Service (EMS) systems worldwide handle emergency rescues as well as patient transports and dispatchers need to assign ambulances to incidents manually throughout the day. The management of the complex system together with the manual assignments can easily create stress for and pressure on the dispatchers. Mathematical algorithms can help improving the dispatching quality, but then dispatchers still need to choose the best-fitting algorithm and furthermore, trust the algorithm’s dispatching suggestion. We propose an assistant that can support the EMS dispatchers. The assistant offers explanations for the choice of the algorithm as well as the dispatching suggestion in order to increase the dispatchers’ trust and decrease their stress. We ground the assistant’s design in Information Systems as well as Operations Research literature and thus, show how interdisciplinary service research can contribute in designing artefacts for complex service systems to solve real-world problems

The Effects Of Recommendation Conflict On User’S Adoption Intention Toward Virtual Salespersons: A Principal-Agent Perspective

Virtual salesperson (VS) has been increasingly implemented on many Websites to provide online users with valuable shopping advice, because it has been proved to alleviate users’ cognitive overload and increase their decision quality. Thus, it has widely caught researchers’ attention to investigate what factors can increase user’s intention to adopt. However, there is little research examining the impact of another information resource on VS adoption intention when recommendation information conflict occurs. This study draws on principle-agent perspective to investigate whether online customer reviews have potential to arouse users’ concern about information asymmetry and the fear of VS opportunism. The research result should be of interest to academic researchers, developers of VSs, providers of VSs, and Webstores

Do Different Data Analytics Impact Auditors\u27 Decisions?

Global stakeholders have expressed interest in increasing the use of data analytics throughout the audit process. While data analytics offer great promise in identifying auditrelevant information, auditors may not use this information to its full potential, resulting in a missed opportunity for possible improvements to audit quality. This article summarizes a study by Koreff (2022) that examines whether conclusions from different types of data analytical models (anomaly vs. predictive) and data analyzed (financial vs. non-financial), result in different auditor decisions. Findings suggest that when predictive models are used and identify a risk of misstatement, auditors increase budgeted audit hours more when financial data is analyzed than when non-financial data is analyzed. However, when anomaly models are used and identify a risk of misstatement, auditors’ budgeted hours do not differ based on the type of data analyzed. These findings provide evidence that different data analytics do not uniformly impact auditors’ decisions