“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

Investigating the Impact of Recommendation Agents on E-commerce Ecosystem

The influence of recommendation agents on e-commerce ecosystem is profound. Technological impact of predictive intelligence could be explained more reasonably by taking a collective perspective. However, the ecosystem perspective has only served as a prologue for discussion regarding technological influence. The lack of research development associated with the technological influence on business in the ecological lens has constrained our understanding of the penetration and the role of technology in business ecosystem evolution. This paper therefore focuses on the impact of recommendation agents for online shopping environment on e-commerce ecosystem. Moreover, this paper observes and explains the phenomena that most participants in the e-commerce ecosystem are taking recommendation agents as one of the strategic technological investments towards further development as a common goal

We know what you want to buy:a demographic-based system for product recommendation on microblogs

Product recommender systems are often deployed by e-commerce websites to improve user experience and increase sales. However, recommendation is limited by the product information hosted in those e-commerce sites and is only triggered when users are performing e-commerce activities. In this paper, we develop a novel product recommender system called METIS, a MErchanT Intelligence recommender System, which detects users' purchase intents from their microblogs in near real-time and makes product recommendation based on matching the users' demographic information extracted from their public profiles with product demographics learned from microblogs and online reviews. METIS distinguishes itself from traditional product recommender systems in the following aspects: 1) METIS was developed based on a microblogging service platform. As such, it is not limited by the information available in any specific e-commerce website. In addition, METIS is able to track users' purchase intents in near real-time and make recommendations accordingly. 2) In METIS, product recommendation is framed as a learning to rank problem. Users' characteristics extracted from their public profiles in microblogs and products' demographics learned from both online product reviews and microblogs are fed into learning to rank algorithms for product recommendation. We have evaluated our system in a large dataset crawled from Sina Weibo. The experimental results have verified the feasibility and effectiveness of our system. We have also made a demo version of our system publicly available and have implemented a live system which allows registered users to receive recommendations in real time

The behavioural impact of a visually represented virtual assistant in a selfservice checkout context

Our research investigated whether the presence of an interface agent - or virtual assistant (VA) - in a self-service checkout context has behavioural effects on the transaction process during particular tasks. While many participants claimed to have not noticed a VA within the self-service interface, behaviour was still affected, i.e. fewer people made errors with the VA present than in the voice-only and control conditions. The results are explained as reflective of an unconscious observation of non-verbal cues exhibited by the VA. The results are discussed in relation to possible behavioural outcomes of VA presence.</p

Incorporating Social Presence in the Design of the Anthropomorphic Interface of Recommendation Agents: Insights from an fMRI Study

Recommendation agents (RAs) are regularly used in online environments to give consumers advice on products. Since social components of human-like RAs (humanoid avatars) are important components in their adoption and use, this study focuses on how the design of the anthropomorphic interface of RAs in terms of social demographics, namely ethnicity and gender, can enhance the RA’s social presence to facilitate their adoption. Since social presence has been shown in the literature to predict the adoption and use of RAs, we examine whether match or mismatch in terms of the anthropomorphic RA’s ethnicity and gender can enhance the user’s social interaction with an RA. To overcome concerns of social desirability bias and political correctness when users assess the social presence of RAs that vary in their ethnicity and gender, we conducted a functional Magnetic Resonance Imaging (fMRI) study to complement a traditional behavioral experiment. Our goal was to explain prior behavioral findings that showed that ethnicity (as opposed to gender) match is associated with higher social presence, particularly among women. Specifically, brain activity was captured in an fMRI scanner while users who varied on their ethnicity and gender to either match or mismatch the ethnicity and gender of four RAs evaluated each of the RAs on their social presence. Besides contributing to the neuroscience literature by identifying the brain activations that relate to social presence, the fMRI results shed light on the nature of social presence and explain earlier behavioral findings by showing gender differences in the neural correlates of social presence in terms of ethnicity and gender match and mismatch. Implications on designing anthropomorphic interfaces to embody social demographics to enhance social presence are discussed

Match or Mismatch? How Matching Personality and Gender between Voice Assistants and Users Affects Trust in Voice Commerce

Despite the ubiquity of voice assistants (VAs), they see limited adoption in the form of voice commerce, an online sales channel using natural language. A key barrier to the widespread use of voice commerce is the lack of user trust. To address this problem, we draw on similarity-attraction theory to investigate how trust is affected when VAs match the user’s personality and gender. We conducted a scenario-based experiment (N = 380) with four VAs designed to have different personalities and genders by customizing only the auditory cues in their voices. The results indicate that a personality match increases trust, while the effect of a gender match on trust is non-significant. Our findings contribute to research by demonstrating that some types of matches between VAs and users are more effective than others. Moreover, we reveal that it is important for practitioners to consider auditory cues when designing VAs for voice commerce

Different Views and Evaluations of IT Artifacts

The introduction and adoption of a multitude of new and interactive information technology (IT) artifacts has impacted adoption research. Rather than solely functioning as productivity tools, new IT artifacts assume the roles of interaction mediators and social actors. This paper describes these varying roles, and discusses the type of perceptions users form when using them. Further, the paper proposes and distinguishes between four foci of how the different types of artifacts are evaluated across cognitive, relational, social, and emotional beliefs. A theoretical model is developed that maps the different views of IT artifacts to the four distinct types of evaluations, and a number of propositions are presented

On Conversational Agents in Information Systems Research: Analyzing the Past to Guide Future Work

Conversational agents (CA), i.e. software that interacts with its users through natural language, are becoming increasingly prevalent in everyday life as technological advances continue to significantly drive their capabilities. CA exhibit the potential to support and collaborate with humans in a multitude of tasks and can be used for innovation and automation across a variety of business functions, such as customer service or marketing and sales. Parallel to the increasing popularity in practice, IS researchers have engaged in studying a variety of aspects related to CA in the last few years, applying different research methods and producing different types of theories. In this paper, we review 36studies to assess the status quo of CA research in IS, identify gaps regarding both the studied aspects as well as applied methods and theoretical approaches, and propose directions for future work in this research area

“I Am Here to Assist You Today”: The Role of Entity, Interactivity and Experiential Perceptions in Chatbot Persuasion

Online users are increasingly exposed to chatbots as one form of AI-enabled media technologies, employed for persuasive purposes, e.g., making product/service recommendations. However, the persuasive potential of chatbots has not yet been fully explored. Using an online experiment (N = 242), we investigate the extent to which communicating with a stand-alone chatbot influences affective and behavioral responses compared to interactive Web sites. Several underlying mechanisms are studied, showing that enjoyment is the key mechanism explaining the positive effect of chatbots (vs. Web sites) on recommendation adherence and attitudes. Contrary to expectations, perceived anthropomorphism seems not to be particularly relevant in this comparison

DESIGN FOR FAST REQUEST FULFILLMENT OR NATURAL INTERACTION? INSIGHTS FROM AN EXPERIMENT WITH A CONVERSATIONAL AGENT

Conversational agents continue to permeate our lives in different forms, such as virtual assistants on mobile devices or chatbots on websites and social media. The interaction with users through natural language offers various aspects for researchers to study as well as application domains for practitioners to explore. In particular their design represents an interesting phenomenon to investigate as humans show social responses to these agents and successful design remains a challenge in practice. Compared to digital human-to-human communication, text-based conversational agents can provide complementary, preset answer options with which users can conveniently and quickly respond in the interaction. However, their use might also decrease the perceived humanness and social presence of the agent as the user does not respond naturally by thinking of and formulating a reply. In this study, we conducted an experiment with N=80 participants in a customer service context to explore the impact of such elements on agent anthropomorphism and user satisfaction. The results show that their use reduces perceived humanness and social presence yet does not significantly increase service satisfaction. On the contrary, our findings indicate that preset answer options might even be detrimental to service satisfaction as they diminish the natural feel of human-CA interaction