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.

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.

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.

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.

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.

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.

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.