A lack of self-awareness of communicative behaviours can lead to disadvantages in important interactions. Video recordings as a tool for self-observation have been widely adopted to initiate behaviour change and reflection. Seeing oneself in a recording can lead to negative affect. Forcing an external perspective can lead to cognitive dissonance. Avatars and virtual agents have the advantage that they can copy a human's behaviour while potentially avoiding this dissonance. To explore the design space of mimicking agents, we set up a user study where a video baseline is compared to agent-mediated conditions ranging from idle non-verbal behaviour to complete mimicry of the voice and face. We show that participants gain increased self-awareness from seeing themselves mediated through the virtual agent. We further discuss qualitative observations for the future design of systems that aid in self-reflection, and particularly note that partial mimicry seems to be less appreciated than full mimicry.

Contextual information is a prerequisite for timely offering of personalized decision support and recommendation. Yet, research on context-aware recommender systems (CARS) does not appear to be thriving, and finding public datasets containing context factors is a challenging task. We can make various assumptions about why this drop in research interest happened – be it ethical considerations or the popularity of opaque deep learning models that merely consider context in an implicit way. This is an unwelcome development. We argue that continued effort must be put on the creation of suitable datasets. Furthermore, we see significant opportunities in the development of next-generation CARS in the space of interactive AI assistants powered by Large Language Models.

Background: Generalized anxiety disorder (GAD) refers to extreme, uncontrollable, and persistent worry and anxiety. The disorder is known to affect the social functioning and well-being of millions of people, but despite its prevalence and burden to society, it has proven difficult to identify unique behavioral markers. Interestingly, the worrying behavior observed in GAD is argued to stem from a verbal linguistic process. Therefore, the aim of the present study was to investigate if GAD can be predicted from the language people use to put their anxious worries into words. Given the importance of avoidance sensitivity (a higher likelihood to respond anxiously to novel or unexpected triggers) in GAD, this study also explored if prediction accuracy increases when individual differences in behavioral avoidance and approach sensitivity are taken into account.

Method: An expressive writing exercise was used to explore whether GAD can be predicted from linguistic characteristics of written narratives. Specifically, 144 undergraduate student participants were asked to recall an anxious experience during their university life, and describe this experience in written form. Clinically validated behavioral measures for GAD and self-reported sensitivity in behavioral avoidance/inhibition (BIS) and behavioral approach (BAS), were collected. A set of classification experiments was performed to evaluate GAD predictability based on linguistic features, BIS/BAS scores, and a concatenation of the two.

Results: The classification results show that GAD can, indeed, be successfully predicted from anxiety-focused written narratives. Prediction accuracy increased when differences in BIS and BAS were included, which suggests that, under those conditions, negatively valenced emotion words and words relating to social processes could be sufficient for recognition of GAD.

Conclusions: Undergraduate students with a high GAD score can be identified based on their written recollection of an anxious experience during university life. This insight is an important first step toward development of text-based digital health applications and technologies aimed at remote screening for GAD. Future work should investigate the extent to which these results uniquely apply to university campus populations or generalize to other demographics.

This study investigates whether an agent-based Negotiation Training System (NTS) can teach women Strategic Empathy - a recently introduced negotiation strategy based on perspective taking - and whether this can improve their negotiation performance. Developed and tested through an interaction-based real-time experiment was a NTS that integrated instructions on how to utilize Strategic Empathy. Women in the experimental group showed significantly higher levels of perspective-taking compared to the control group, and their understanding and use of Strategic Empathy increased over time. Also, a significant positive effect was found of Strategic Empathy on women's self-efficacy. No significant positive effect was found of Strategic Empathy on persistence. The high cognitive load of the experiment and a lack of intrinsic motivation may have caused this finding. Overall, this work demonstrates the applicability of using NTS to teach Strategic Empathy, and its effectiveness for enhancing women's self-efficacy in salary negotiations.

We assess the case of the abrupt discontinuation of the three-in-one policy, a high-occupancy vehicle (HOV) restriction, in Jakarta, with the objective of mapping potential interdependencies in the transportation system. Statistical investigation of the passenger volume in the bus rapid transit (BRT) system in the whole city before and after the policy change revealed a significant increase in the number of passengers during peak hours, especially in the evening period. The extent of the increase, however, depended on whether the area had been subject to the initial policy restriction. The case of sudden discontinuation of the three-in-one policy in Jakarta illustrates how a change in policy aimed at a single transportation mode may spill over to alternative transportation modes. The importance of acknowledging the systemic nature of urban transportation systems when altering policies intended to discourage the use of a single transportation mode within the larger transportation network is discussed.

Manufacturing companies are confronted with challenges due to increasing flexibility requirements and skill gaps. Augmented Reality applications offer an efficient way to overcome these tensions by enhancing the interaction between people and technology. The positive effects of Augmented Reality solutions are often described in individual models in the scientific literature. This research-in-progress aims to aggregate the empirical findings in the usage of Augmented Reality solutions in manufacturing environments. A meta-analysis is conducted to synthesise several small studies into one large study to achieve this. In particular, the meta-analysis will focus on the impact of Augmented Reality applications on cognitive load levels. Furthermore, the effect on processing time and error rates will be evaluated. Initial results of the meta-analysis will be expected and reported at this year’s NeuroIS Retreat.

The present research explored to what extent user engagement in proactive recommendation scenarios is influenced by the accuracy of recommendations, concerns with information privacy, and trait personality. We hypothesized that people’s self-reported information privacy concerns would matter more when they received accurate (vs. inaccurate) proactive recommendations, because these pieces of advice would seem fair to them. We further hypothesized that this would particularly be the case for people high on the social personality trait Extraversion, who are by inclination prone to behaving in a more socially engaging manner. We put this to the test in a controlled experiment, in which users received manipulated proactive recommendations of high or low accuracy on their smartphone. Results indicated that information privacy concerns positively influenced a user’s engagement with proactive recommendations. Recommendation accuracy influenced user engagement in interaction with information privacy concerns and personality traits. Implications for the design of human-computer interaction for recommender systems are addressed.

Rating summary statistics are basic aggregations that reflect users’ assessments of experienced products and services in numerical form. Thus far, scholars primarily investigated textual reviews, but dedicated considerably less time and effort exploring the potential impact of plain rating summary statistics on people’s choice behavior. Notwithstanding their fundamental nature, however, rating summary statistics also are relevant to electronic commerce in general, and to e-tourism in particular. In this work, we attempted to fill this void, by exploring the effects of different types of rating attributes (the mean rating value, the overall number of ratings, and the bimodality of rating distributions) on hotel choice behavior. We also investigated whether individual differences in the cause of people’s maximizing behavioral tendency moderated the effect of rating summary statistics on hotel choice behavior. Results of an eye-tracked conjoint experiment show that people’s high or low on decision difficulty as the cause of maximization determined whether and how rating summary statistics have an impact on the choice between hotels. Implications for the tourism and hospitality domain are addressed.

Rating-based summary statistics are ubiquitous in e-commerce, and often are crucial components in personalized recommendation mechanisms. Especially visual rating summarizations have been identiied as important means to explain, why an item is presented or proposed to an user. Largely left unexplored, however, is the issue to what extent the descriptives of these rating summary statistics inluence decision making of the online consumer. Therefore, we conducted a series of two conjoint experiments to explore how diferent summarizations of rating distributions (i.e., in the form of number of ratings, mean, variance, skewness, bimodality, or origin of the ratings) impact users' decision making. In a irst study with over 200 participants, we identiied that users are primarily guided by the mean and the number of ratings, and - to lesser degree - by the variance and origin of a rating. When probing the maximizing behavioral tendencies of our participants, other sensitivities regarding the summary of rating distributions became apparent. We thus instrumented a follow-up eye-tracking study to explore in more detail, how the choices of participants vary in terms of their decision making strategies. This second round with over 40 additional participants supported our hypothesis that users, who usually experience higher decision diiculty, follow compensatory decision strategies, and focus more on the decisions they make. We conclude by outlining how the results of these studies can guide algorithm development, and counterbalance presumable biases in implicit user feedback.

Research on understanding, developing and assessing personalisation systems is spread over multiple disciplines and builds on methodologies and findings from several different research fields and traditions, such as Artificial Intelligence (AI), Machine Learning (ML), Human–Computer Interaction (HCI), and User Modelling based on (applied) social and cognitive psychology. The fields of AI and ML primarily focus on the optimisation of personalisation applications, and concentrate on creating ever more accurate algorithmic decision makers and prediction models. In the fields of HCI and Information Systems, scholars are primarily interested in the phenomena around the use and interaction with personalisation systems, while Cognitive Science (partly) delivers the theoretical underpinnings for the observed effects. The aim and contribution of this work is to put together the pieces about the impact of personalisation and recommendation systems from these different backgrounds in order to formulate a research agenda and provide a perspective on future developments.

Human judgment, an almost inextricable ingredient in demand forecasting, introduces many unintentional and intentional biases to the forecasting and operations planning process. In the present research, we isolate intentional biases from this process and relate them to heterogeneous departmental roles and incentives. Through a laboratory experiment, which simulates forecasting operations planning in an interdepartmental decision-making context, we examine the effects of departmental roles, incentives and various weighting schemes on forecasting behavior and performance. We find that departmental roles, even without role-specific incentives, entail intentional biases of 8% of the forecast, and that role-specific incentives increase these biases to 14%. We further test the claim that accuracy-weighted schemes can remove biases in forecasting, and conclude that they halve, but don't fully remove them. Finally, a weighting scheme that explicitly corrects biased inputs shows great promise in reducing intentional and unintentional biases. In our experiment, this scheme reduces biases by 35%. This shows the importance of disentangling intentional and unintentional biases for more effective forecasting adjustments. Our insights have substantial ramifications for the design of the forecasting operations planning process in dynamic business environments determined by high levels of role- and incentive-based heterogeneity.

Collaborative filtering systems heavily depend on user feedback expressed in product ratings to select and rank items to recommend. These summary statistics of rating values carry two important descriptors about the assessed items, namely the total number of ratings and the mean rating value. In this study we explore how these two signals influence the decisions of online users based on choice-based conjoint experiments. Results show that users are more inclined to follow the mean indicator as opposed to the total number of ratings. Empirical results can serve as an input to developing algorithms that foster items with a, consequently, higher probability of choice based on their rating summarizations or their it explainability due to these ratings when ranking recommendations.

Collaborative filtering systems heavily depend on user feedback expressed in product ratings to select and rank items to recommend. In this study we explore how users value different collaborative explanation styles following the user-based or item-based paradigm. Furthermore, we explore how the characteristics of these rating summarizations, like the total number of ratings and the mean rating value, influence the decisions of online users. Results, based on a choice-based conjoint experimental design, show that the mean indicator has a higher impact compared to the total number of ratings. Finally, we discuss how these empirical results can serve as an input to developing algorithms that foster items with a, consequently, higher probability of choice based on their rating summarizations or their explainability due to these ratings when ranking recommendations.

The goal of the present study was to investigate how satisfied individuals are with the final outcome of a group decision-making process on a joint travel destination. Using an experimental paradigm (N total = 200, N groups = 55) it was obvious to hypothesize that individuals would especially be satisfied with the final group decision when it matched their own initial travel preference and that they would be dissatisfied in case it mismatched their initial preference. However, in addition the influence of personality and group dynamics differences (Thomas-Kilmann Conflict Mode Instrument, Five Factor Model) as well as travel types of the individual decision maker on the satisfaction level with the group decision outcome as the dependent variable were further researched. The paper concludes with implications for e-tourism, especially with regards to the development of interactive tools for group travel.

Car sharing services gain momentum as a potential alternative to various modes of transportation, including privately owned cars. This trend goes hand in hand with a renewed interest in the sharing economy, which has as essential premise that product ownership is of minor relevance. Using an online experiment, this study investigates if individual differences in psychological ownership influence the effects of well-known instrumental car attributes (price, parking convenience, and car type) on people's intentions to select a shared car. Results confirmed that instrumental attributes generally impact preferences for car sharing services, and that a low psychological ownership may lead to a higher preference for a shared car under specific circumstances. This suggests that not only instrumental car attributes, but also psychological disposition, specifically psychological ownership, of potential customers need to be taken into consideration when developing measures to stimulate car sharing services in society.

Many artists and designers borrow, cite, or seek inspiration in external source materials in their daily creative practice. The aim of this chapter is to show that imitation of external source material offers creative professionals the opportunity to introduce an element of surprise to the creative act, which may explain why a creative product with very little or no originality whatsoever can nevertheless gain reputation as being creative. The literature on imitation in psychology and the humanities will be reviewed in parallel to a recent suggestion in creativity research to give more prominence to the criterion of surprise in the study of creativity. The potential benefit of imitation for creativity in art and design will be illustrated with a description of the working practices of the prominent painter Gerhard Richter and the famous car designer J Mays – two contemporary creative professionals renowned for usage of external source material in their own creative work.

Most research on group recommender systems relies on the assumption that individuals have conflicting preferences; in order to generate group recommendations the system should identify a fair way of aggregating these preferences. Both empirical studies and theoretical frameworks have tried to identify the most effective preference aggregation techniques without coming to definite conclusions. In this paper, we propose to approach group recommendation from the group dynamics perspective and analyze the group decision making process for a particular task (in the travel domain). We observe several individual and group properties and correlate them to choice satisfaction. Supported by these initial results we therefore advocate for the development of new group recommendation techniques that consider group dynamics and support the full group decision making process.