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Article Effects of AI Virtual Anchors on Brand Image and Loyalty: Insights from Perceived Value Theory and SEM-ANN AnalysisZhu · 2025AI virtual anchors are an emerging innovation that are gaining significant at- tention, as they hold promising applications across various fields. This study examines how users perceive live product selling by AI virtual anchors and its impact on brand image and brand loyalty. A two-stage PLS-SEM and ANN approach was employed to analyze data from a sample of 336 individuals in China who had experienced and utilized AI virtual anchors for purchases during branded live streaming sessions. The findings indicate that perceived usefulness, perceived enjoyment, and novelty positively impact brand image, with artificial neural network (ANN) analysis identifying brand image as the primary predictor. Furthermore, brand image acts as a mediator between these user perceptions and brand loyalty. These insights offer brand managers a strategic approach to utilize AI virtual anchors for fostering a positive brand image and building loyal customer bases. The study also contributes to the academic understanding of consumer behavior and brand management in the context of AI.

UX of AI & PersonalizationResearch Papers

Is trust in artificial intelligence systems related to user personality? Review of empirical evidence and future research directionsRene Riedl · 2022Artificial intelligence (AI) refers to technologies which support the execution of tasks normally requiring human intelligence (e.g., visual perception, speech recognition, or decision-making). Examples for AI systems are chatbots, robots, or autono- mous vehicles, all of which have become an important phenomenon in the economy and society. Determining which AI system to trust and which not to trust is critical, because such systems carry out tasks autonomously and influence human- decision making. This growing importance of trust in AI systems has paralleled another trend: the increasing understanding that user personality is related to trust, thereby affecting the acceptance and adoption of AI systems. We developed a frame- work of user personality and trust in AI systems which distinguishes universal personality traits (e.g., Big Five), specific personality traits (e.g., propensity to trust), general behavioral tendencies (e.g., trust in a specific AI system), and specific behaviors (e.g., adherence to the recommendation of an AI system in a decision-making context). Based on this framework, we reviewed the scientific literature. We analyzed N = 58 empirical studies published in various scientific disciplines and developed a “big picture” view, revealing significant relationships between personality traits and trust in AI systems. However, our review also shows several unexplored research areas. In particular, it was found that prescriptive knowledge about how to design trustworthy AI systems as a function of user personality lags far behind descriptive knowledge about the use and trust effects of AI systems. Based on these findings, we discuss possible directions for future research, including adaptive systems as focus of future design science research.

UX of AI & PersonalizationResearch Papers

Anchoring Bias Affects Mental Model Formation and User Reliance in Explainable AI SystemsMahsan Nourani · 2021EXplainable Artificial Intelligence (XAI) approaches are used to bring transparency to machine learning and artificial intelligence models, and hence, improve the decision-making process for their end-users. While these methods aim to improve human understand- ing and their mental models, cognitive biases can still influence a user’s mental model and decision-making in ways that system de- signers do not anticipate. This paper presents research on cognitive biases due to ordering effects in intelligent systems. We conducted a controlled user study to understand how the order of observing sys- tem weaknesses and strengths can affect the user’s mental model, task performance, and reliance on the intelligent system, and we investigate the role of explanations in addressing this bias. Using an explainable video activity recognition tool in the cooking domain, we asked participants to verify whether a set of kitchen policies are being followed, with each policy focusing on a weakness or a strength. We controlled the order of the policies and the presence of explanations to test our hypotheses. Our main finding shows that those who observed system strengths early-on were more prone to automation bias and made significantly more errors due to positive first impressions of the system, while they built a more accurate mental model of the system competencies. On the other hand, those who encountered weaknesses earlier made significantly fewer er- rors since they tended to rely more on themselves, while they also underestimated model competencies due to having a more negative first impression of the model. Our work presents strong findings that aim to make intelligent system designers aware of such biases when designing such tools. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. IUI ’21, April 14–17, 2021, College Station, TX, USA © 2021 Association for Computing Machinery. ACM ISBN 978-1-4503-8017-1/21/04...$15.00 https://doi.org/10.1145/3397481.34

UX of AI & PersonalizationResearch Papers

How Should AI Systems Talk to Users when Collecting their Personal Information? Efects of Role Framing and Self-Referencing on Human-AI InteractionMengqi Liao · 2021AI systems collect our personal information in order to provide per­ sonalized services, raising privacy concerns and making users leery. As a result, systems have begun emphasizing overt over covert collection of information by directly asking users. This poses an important question for ethical interaction design, which is ded­ icated to improving user experience while promoting informed decision-making: Should the interface tout the benefts of infor­ mation disclosure and frame itself as a help-provider? Or, should it appear as a help-seeker? We decided to fnd out by creating a mockup of a news recommendation system called Mindz and conducting an online user study (N=293) with the following four variations: AI system as help seeker vs. help provider vs. both vs. neither. Data showed that even though all participants received the same recommendations, power users tended to trust a help-seeking Mindz more whereas non-power users favored one that is both help-seeker and help-provider. CCS CONCEPTS • Human-centered computing → Human computer interaction (HCI); Empirical studies in HCI.

UX of AI & PersonalizationResearch Papers

How Do Visual Explanations Foster End Users' Appropriate Trust in Machine Learning?Fumeng Yang · 2020We investigated the effects of example-based explanations for a machine learning classifier on end users’ appropriate trust. We explored the effects of spatial layout and visual representation in an in-person user study with 33 participants. We measured partici- pants’ appropriate trust in the classifier, quantified the effects of different spatial layouts and visual representations, and observed changes in users’ trust over time. The results show that each expla- nation improved users’ trust in the classifier, and the combination of explanation, human, and classification algorithm yielded much better decisions than the human and classification algorithm sepa- rately. Yet these visual explanations lead to different levels of trust and may cause inappropriate trust if an explanation is difficult to un- ∗Fumeng Yang was a PhD intern at Pacific Northwest National Laboratory when conducting this research. †Jean Scholtz retired from Pacific Northwest National Laboratory September 2018. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). IUI ’20, March 17–20, 2020, Cagliari, Italy © 2020 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-7118-6/20/03. [https://doi.org/10.1145/3377325.3377480](https://doi.org/10.1145/3377325.3377480) derstand. Visual representation and performance feedback strongly affect users’ trust, and spatial layout shows a moderate effect. Our results do not support that individual differences (e.g., propensity to trust) affect users’ trust in the classifier. This work advances the state-of-the-art in trust-able machine learning and informs the design and appropriate use of automated systems. CCS CONCEPTS • Human-centered computing →Empirical studies in HCI; Information visualization; Empirical studies in visualization; Visualization design and evaluation methods; • Computing method- ologies →Supervised learning by classification.

UX of AI & PersonalizationResearch Papers

Progressive Disclosure: When, Why, and How Do Users Want Algorithmic Transparency Information?Arron Springer, Steve Whittaker · 2020It is essential that users understand how algorithmic decisions are made, as we increasingly delegate important decisions to intelligent systems. Prior work has often taken a techno-centric approach, focusing on new computational techniques to support transparency. In contrast, this article employs empirical methods to better understand user reactions to transparent systems to motivate user-centric designs for transparent systems. We assess user reactions to transparency feedback in four studies of an emotional analytics system. In Study 1, users anticipated that a transparent system would perform better but unexpectedly retracted this evaluation after experience with the system. Study 2 offers an explanation for this paradox by showing that the benefits of transparency are context dependent. On the one hand, transparency can help users form a model of the underlying algorithm's operation. On the other hand, positive accuracy perceptions may be undermined when transparency reveals algorithmic errors. Study 3 explored real-time reactions to transparency. Results confirmed Study 2, in showing that users are both more likely to consult transparency information and to experience greater system insights when formulating a model of system operation. Study 4 used qualitative methods to explore real-time user reactions to motivate transparency design principles. Results again suggest that users may benefit from initially simplified feedback that hides potential system errors and assists users in building working heuristics about system operation. We use these findings to motivate new progressive disclosure principles for transparency in intelligent systems and discuss theoretical implications.

UX of AI & PersonalizationResearch Papers

History and Future of Human AutomationJanssen · 2019We review the history of human-automation interaction research, assess its current status and identify future directions. We start by reviewing articles that were published on this topic in the International Journal of Human-Computer Studies during the last 50 years. We find that over the years, automated systems have been used more frequently (1) in time-sensitive or safety-critical settings, (2) in embodied and situated systems, and (3) by non-professional users. Looking to the future, there is a need for human-automation interaction research to focus on (1) issues of function and task allocation between humans and machines, (2) issues of trust, incorrect use, and confusion, (3) the balance between focus, divided attention and attention management, (4) the need for interdisciplinary approaches to cover breadth and depth, (5) regulation and explainability, (6) ethical and social dilemmas, (7) allowing a human and humane experience, and (8) radically different human-automation inter- action.

UX of AI & PersonalizationResearch Papers

Explanations as Mechanisms for Supporting Algorithmic TransparencyEmilee Rader · 2018Transparency can empower users to make informed choices about how they use an algorithmic decision-making system and judge its potential consequences. However, transparency is often conceptualized by the outcomes it is intended to bring about, not the specifics of mechanisms to achieve those out- comes. We conducted an online experiment focusing on how different ways of explaining Facebook’s News Feed algorithm might affect participants’ beliefs and judgments about the News Feed. We found that all explanations caused participants to become more aware of how the system works, and helped them to determine whether the system is biased and if they can control what they see. The explanations were less effective for helping participants evaluate the correctness of the system’s output, and form opinions about how sensible and consistent its behavior is. We present implications for the design of trans- parency mechanisms in algorithmic decision-making systems based on these results. Author Keywords algorithmic decision-making; transparency; explanations ACM Classification Keywords H.5.m. Information Interfaces and Presentation (e.g. HCI): Miscellaneous

UX of AI & PersonalizationResearch Papers

A systematic review and taxonomy of explanations in decision support and recommender systemsIngrid Nunes · 2017With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems. A key requirement for the success and adoption of such systems is that users must trust system choices or even fully automated decisions. To achieve this, explanation facilities have been widely investigated as a means of establishing trust in these systems since the early years of expert systems. With today’s increasingly sophisticated machine learning algorithms, new challenges in the context of explanations, accountability, and trust towards such systems constantly arise. In this work, we systematically review the literature on explanations in advice-giving systems. This is a family of systems that includes recommender systems, which is one of the most successful classes of advice- giving software in practice. We investigate the purposes of explanations as well as how they are generated, presented to users, and evaluated. As a result, we derive a novel comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems. The taxonomy includes a variety of different facets, such as explanation objective, responsiveness, content and presentation. Moreover, we identified several challenges that remain unaddressed so far, for example related to fine-grained issues associated with the presentation of explanations and how explanation facilities are evaluated. B Ingrid Nunes ingridnunes@inf.ufrgs.br Dietmar Jannach dietmar.jannach@tu-dortmund.de 1 Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil 2 TU Dortmund, Dortmund, Germany 123 394 I. Nunes, D. Jannach

UX of AI & PersonalizationResearch Papers

“Algorithms ruin everything”: #RIPTwitter, Folk Theories, and Resistance to Algorithmic Change in Social MediaDarren Gergle · 2017As algorithmically-driven content curation has become an increasingly common feature of social media platforms, user resistance to algorithmic change has become more frequent and visible. These incidents of user backlash point to larger issues such as inaccurate understandings of how algorithmic systems work as well as mismatches between designer and user intent. Using a content analysis of 102,827 tweets from #RIPTwitter, a recent hashtag-based backlash to rumors about introducing algorithmic curation to Twitter’s timeline, this study addresses the nature of user resistance in the form of the complaints being expressed, folk theories of the algorithmic system espoused by users, and how these folk theories potentially frame user reactions. We find that resistance to algorithmic change largely revolves around expectation violation, with folk theories acting as frames for reactions such that more detailed folk theories are expressed through more specific reactions to algorithmic change. Author Keywords Algorithms; algorithm awareness; folk theories; technology continuance; user resistance; social media; algorithmic curation; expectation violation; machine classification ACM Classification Keywords H.5.m. Information interfaces and presentation (e.g., HCI):

UX of AI & PersonalizationResearch Papers

How Much Information? Effects of Transparency on Trust in an Algorithmic InterfaceRene Kizilcec · 2016The rising prevalence of algorithmic interfaces, such as cu- rated feeds in online news, raises new questions for designers, scholars, and critics of media. This work focuses on how trans- parent design of algorithmic interfaces can promote awareness and foster trust. A two-stage process of how transparency affects trust was hypothesized drawing on theories of infor- mation processing and procedural justice. In an online field experiment, three levels of system transparency were tested in the high-stakes context of peer assessment. Individuals whose expectations were violated (by receiving a lower grade than expected) trusted the system less, unless the grading algorithm was made more transparent through explanation. However, providing too much information eroded this trust. Attitudes of individuals whose expectations were met did not vary with transparency. Results are discussed in terms of a dual process model of attitude change and the depth of justification of per- ceived inconsistency. Designing for trust requires balanced interface transparency—not too little and not too much. ACM Classification Keywords H.5.2. Information Interfaces and Presentation (e.g. HCI): User Interfaces; K.3.1. Computers and Education: Computer Uses in Education. Author Keywords Interface Design; Algorithm Awareness; Attitude Change; Transparency; Trust; Peer Assessment.

UX of AI & PersonalizationResearch Papers

Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic managementMin Kyung Lee · 2013Algorithms increasingly make managerial decisions that people used to make. Perceptions of algorithms, regardless of the algorithms’ actual performance, can significantly influence their adoption, yet we do not fully understand how people perceive decisions made by algorithms as compared with decisions made by humans. To explore perceptions of algo- rithmic management, we conducted an online experiment using four managerial decisions that required either mechan- ical or human skills. We manipulated the decision-maker (algorithmic or human), and measured perceived fairness, trust, and emotional response. With the mechanical tasks, algorithmic and human-made decisions were perceived as equally fair and trustworthy and evoked similar emotions; however, human managers’ fairness and trustworthiness were attrib- uted to the manager’s authority, whereas algorithms’ fairness and trustworthiness were attributed to their perceived efficiency and objectivity. Human decisions evoked some positive emotion due to the possibility of social recognition, whereas algorithmic decisions generated a more mixed response – algorithms were seen as helpful tools but also possible tracking mechanisms. With the human tasks, algorithmic decisions were perceived as less fair and trustworthy and evoked more negative emotion than human decisions. Algorithms’ perceived lack of intuition and subjective judg- ment capabilities contributed to the lower fairness and trustworthiness judgments. Positive emotion from human decisions was attributed to social recognition, while negative emotion from algorithmic decisions was attributed to the dehumanizing experience of being evaluated by machines. This work reveals people’s lay concepts of algorithmic versus human decisions in a management context and suggests that task characteristics matter in understanding people’s experiences with algorithmic technologies.

UX of AI & PersonalizationResearch Papers

Measurement of Trust in Automation: A Narrative Review and Reference Guide2021With the rise of automated and autonomous agents, research examining Trust in Automation (TiA) has attracted considerable attention over the last few decades. Trust is a rich and complex construct which has sparked a multitude of measures and approaches to study and understand it. This comprehensive narrative review addresses known methods that have been used to capture TiA. We examined measurements deployed in existing empirical works, categorized those measures into self-report, behavioral, and physiological indices, and examined them within the context of an existing model of trust. The resulting work provides a reference guide for researchers, providing a list of available TiA measurement methods along with the model-derived constructs that they capture including judgments of trustworthiness, trust attitudes, and trusting behaviors. The article concludes with recommendations on how to improve the current state of TiA measurement.

HCI & Adaptive InterfacesResearch Papers