Making accurate, unbiased decisions is critical in high-stakes professions such as law enforcement, intelligence analysis, and medicine, since the decisions can have severe consequences. In this chapter, we discuss what makes persuasive games effective for training professionals to recognize their cognitive biases, improve their knowledge about decision-making biases, and learn ways of mitigating bias. We describe our experience designing three games for professional training in cognitive biases and deception detection. This chapter focuses on the combination of decisionmaking, education, and game theories that drives our design. This is then followed by a discussion of our experiments and measurements for testing the effectiveness of our designs.

Transparent reporting is a cornerstone of credible and reproducible research, yet digital experiments—randomized studies conducted in online or computerized environments—often lack clear reporting standards. To address this gap, we adapt and extend the CONSORT Statement, a widely used checklist for clinical trials, to develop a reporting checklist tailored for digital experiments. While the checklist is broadly applicable across disciplines, we demonstrate its relevance through an empirical application to digital experiments published in two leading Information Systems (IS) journals: Information Systems Research and MIS Quarterly. Our analysis reveals substantial shortcomings in reporting quality, with only 43% of the papers providing sufficient detail on core items. These findings highlight the urgent need for standardized reporting in digital experimentation. The proposed checklist offers a practical tool for authors, reviewers, and editors to enhance the transparency, rigor, and replicability of digital experiments.

As the importance of Human-Computer Interaction (HCI) continues to grow we have witnessed new and exciting avenues for exploration.In 2006, the HCI minitrack was launched to provide an outlet for a variety of HCI research streams from a variety of disciplines.In 2013, we began including the disciplines neuroscience and design science.With the ever-increasing role of information systems in all aspects of society, we moved the minitrack to the Internet and Digital Economy track in 2019, with the focus on the role of Human-Computer Interaction in the Digital Economy.Whereas traditional desktop computers continue to be important and widely used information systems-especially in organizational and home office settings-the scope of HCI research has broadened considerably, with a proliferation of devices, contexts, and form factors. Just as mobile devices-such as smartphones and tablets-enable new forms of interaction while constraining others, so do wearable devices, such as smart watches and fitness trackers.In their homes, people are increasingly interacting with IoT-enabled home automation devices.Likewise, automobiles have evolved from a simple means of transportation toward becoming connected vehicles, using and providing data ranging from entertainment to navigation to vehicle-to-vehicle communications; these new trends-and otherscontribute to the continued need for theory-based HCI research in a variety of contexts and domains.Our aim is to get a truly cross-disciplinary understanding of HCI that informs contemporary research and impacts design practices.The papers selected for the competitive HCI minitrack draw on this rich cross-disciplinary tradition.Given that HCI continues to grow and change, we aim to provide a forum for the exchange of novel thoughts and ideas.We believe that the four papers presented in this minitrack will provide interesting and thought-provoking discussions that will be relevant for both research and practitioners.The accepted papers provide a cross-section of HCI and interface design issues in general contexts, as well as in emerging contexts, such as mobile and automotive information systems.

Usability the extent to which a system can be operated to achieve specified goals with effectiveness, efficiency, and satisfaction is a hallmark of many successful systems. Identifying usability concerns with existing measures, however, can be problematic. We use biased competition theory (BCT) to explain how interfaces with low usability create attentional interference, which can be measured through mouse movement efficiency (MME). We tested our hypotheses in four studies. Study 1 manipulates attentional interference and examines its influence on MME using eye tracking to validate BCT as an appropriate theoretical lens for our research. Study 2 manipulates usability and shows similar markers of attentional interference in MME on a website with two different populations. Study 3 is a field test that compares the reliability of MME in ranking the usability of different components of a commercial web application to rankings produced by a common perceptual measure of usability: perceived ease of use. Collaborating with a Fortune 100 software vendor, Study 4 further demonstrates the efficacy of MME in ranking the usability of various system components compared to a standard industry measure of usability across three different products. The results consistently show that MME can help identify usability differences among parts of systems. The results have implications for facilitating more efficient and temporally precise usability research and mass-deployable usability testing.

Social desirability bias undermines self-report accuracy, necessitating novel approaches to detect and mitigate its impact. This study aimed to investigate the influence of social desirability on questionnaire responses by analyzing mouse cursor movements and answering behaviors. Respondents (n=238) completed a health and wellness questionnaire while their mouse cursor data was recorded. The results revealed that individuals under a higher social desirability treatment exhibited significantly longer response times and slower mouse cursor speeds, supporting the hypothesis that they may engage in more cautious and deliberate responding. However, no significant differences were found in terms of mouse cursor deviations or answer switches between the two groups. These findings suggest that analyzing mouse cursor movements can provide valuable insights into the influence of social desirability bias on questionnaire responses, offering a potentially scalable method for detection and future intervention.

Research on behavioral computing often involves human participants, necessitating informed consent and compliance with regulatory standards like the General Data Protection Regulation (GDPR), overseen by an Institutional Review Board (IRB) or Ethics Committee. Unfortunately, this oversight can cause significant delays and complexity for researchers initiating or expanding a program of research. We advocate adopting an “umbrella” protocol to streamline the IRB review and approval while supporting flexibility as research evolves. First, we describe our experience with a single umbrella protocol used to collect nearly 8,000 responses across seven main studies and 23 sub-studies, demonstrating how the protocol simplified approvals, ensured compliance, and expedited our evolving research agenda. Second, we provide practical guidelines for implementing an umbrella protocol in a way that supports the IRB or Ethics Committee in safeguarding ethical research. This method effectively manages extensive data collections, enabling iterative refinement of a broad research program. Its success highlights potential gains in research productivity, offering a model for efficient and responsible research management.

Real-time assessment of users' cognitive states has practical importance, allowing organizations to infer user behaviors. Realizing its importance, prior studies – specifically those using mouse cursor movements – have applied various theories to answer a similar question, i.e., how does a high cognitive load influence the users' device usage behavior? While numerous activities can increase cognitive load, we argue that the mechanisms behind how humans process information can more holistically be explained using Dual Process Theory (DPT) (i.e., when cognitive load is either low or high) and can be applied under a broad range of usage contexts. Using a within-participant experiment and a simple typing task, we demonstrate that DPT is robust to work by examining DPT and mouse cursor movements. Specifically, users' typing speed and task execution are significantly slower when engaged in the task (System 2) and significantly faster when completing the task with lower cognitive effort and engagement (System 1).

Users' underlying cognitive states govern their behaviors online.An extreme cognitive burden during live system use would negatively influence important user behaviors such as using the system and purchasing a product.Thus, inferring the user's cognitive state has practical significance for the commercialized systems.We use Dual-Process Theory to explain how the mouse cursor movements can effectively measure cognitive load.In an experimental study with five hundred and thirty-four subjects, we induced cognitive burden then monitored mouse cursor movements when the participants answered questions in an online survey.We found that participants' mouse cursor movements slow down when engaged in cognitively demanding tasks.We further derived new measures to infer the state of heightened cognitive load with an overall accuracy of 70.22%.The results enable researchers to measure users' cognitive load with more granularity and present a new, theoretically sound method to assess the user's cognitive state.

In traditional face-to-face classes, conventional wisdom suggests that delivering and watching group project presentations is a valuable learning experience.In this research, we examine the limits of student engagement and learning in an asynchronous online context.Specifically, 249 undergraduate students were assigned to perform peer evaluations of multiple tenminute project presentations.The online learning platform collected objective viewing behavior for each student, allowing us to use viewing time as a proxy for engagement.We also collected self-reported attitudes towards the assignment, finding that while students value providing feedback, they do not consider it a valuable use of their time.Students who engage more are also likely to receive a better final course grade.Finally, students exhibit different types of viewing behavior (i.e., personas) when evaluating multiple videos.Based on these results, we provide suggestions for improving the design of online group presentation and peer-review assignments.

To enable people to interact with online websites and systems, browsers capture a variety of events that occur on the page—such as how a person is moving the computer mouse, what a person clicks on, what a person types, and whether a person is scrolling. These events represent a user's behavior on a page, referred to as the DOM or Document Object Model, and are recorded at a millisecond precision rate (e.g., the exact millisecond timestamp when a key goes down and when it comes back up). Research and practice alike have found that these behavioral events can provide powerful insight into the users' experience, such as whether users are frustrated, and even help distinguish between legitimate and fraudulent users. In this paper, we present six best practices for responsibly collecting these digital behavioral biometric data to help protect user privacy as well as encourage proper interpretation. For each principle, we discuss its rationale and practical application.

Trace data—users’ digital records when interacting with technology—can reveal their cognitive dynamics when making decisions on websites in real time. Here, we present a trace­data method, analyzing movements captured via a computer mouse, to assess potential fraud when filling out an online form. In contrast to existing fraud­detection methods, which analyze information after submission, mouse­movement traces can capture the cognitive deliberations as possible indicators of fraud as it is happening. We report two controlled studies using different tasks, where participants could freely commit fraud to benefit themselves financially. As they performed the tasks, we captured mouse­cursor movement data and found that participants who entered fraudulent responses moved their mouse significantly more slowly and with greater deviation. We show that the extent of fraud matters such that more extensive fraud increases movement deviation and decreases movement speed. These results demonstrate the efficacy of analyzing mouse­movement traces to detect fraud during online transactions in real time, enabling organizations to confront fraud proactively as it is happening at internet scale. Our method of analyzing actual user behaviors in real time can complement other behavioral methods in the context of fraud and a variety of other contexts and settings.

This article articulates the importance of developing a Theory of Online Visual Aesthetics in the IS tradition, and demonstrates that to date the field is still lacking a theoretical model for explaining online visual aesthetics. To address this gap in the literature the authors propose the Online Visual Aesthetics Theoretical Model, drawn from over 100 years of literature in complementary disciplines. The Online Visual Aesthetics Theory Model proposes that the perception of the composition of an online medium is directly influenced by the eight web design categories: Graphics, Text, Simplicity, Animation, Layout, Unity, Emphasis, and Balance. The variance of each of these web design categories may then be explained by the more traditional seven primary design dimensions: Lines, Shapes, Colors, Textures, Forms, Values, and Spaces. The final instantiated Online Visual Aesthetics Theoretical Model is then presented and a program of study is articulated for the evaluation and validation of the proposed theoretical model.

Digital experiments, experiments, which examine an IT artifact and/or largely use information technology (IT) for stimulus presentation and/or response collection, have become a widely used research method for establishing cause­and­effect relationships. Many experiments, however, have been criticized for lacking reproducibility, which can have serious consequences. For example, nonreplicable experiments may lead practitioners to base their decisions on misleading evidence, thereby harming the reputation of science. To improve the replicability of experiments, authors need to report methods and results in a clear, transparent, and comprehensive manner. Hence, to improve the reporting of digital experiments, we have drawn upon the prior literature to develop a reporting checklist and associated guidelines. Specifically, we have drawn on the Consolidated Standards of Reporting Trials (CONSORT), which has been endorsed by more than 600 academic journals. However, because CONSORT was developed to improve the reporting of clinical trials when evaluating the effectiveness and safety of medications or medical devices, it lacks the items and guidelines necessary to comprehensively report digital experiments. As such, we propose the Digital Experiments Reporting Protocol (DERP), which adapts CONSORT as a suitable guide for reporting digital experiments. Applying our checklist to top­tier information systems articles (from Information Systems Research and MIS Quarterly), we found that on average, only 39% report core items in sufficient detail, highlighting the need for a standardized checklist. As such, our checklist provides a valuable tool with which authors and reviewers can improve both study reporting and replicability.

Ensuring organizational regulatory and legal compliance is a challenging, high-stakes management task. Overlooking or underestimating noncompliance in organizations can result in substantial fines, damage to a company's reputation, and, ultimately, loss of business. To help alleviate this risk, we explore whether organizations can assess noncompliance by monitoring users' answers and related mouse cursor movements in an intelligent online questionnaire. Namely, we propose that noncompliant (compared with compliant) individuals will experience more cognitive dissonance on questions about (1) what constitutes noncompliance behavior and (2) what consequences for noncompliance are appropriate. We predict that increased cognitive dissonance in noncompliant individuals will influence their questionnaire responses-as well as their mouse cursor movements-when answering these questions. We collect data to test our hypotheses in a study from individuals who voluntarily chose to cheat on an online task for monetary gain. The results suggest that the responses to these two groups of questions, along with the associated mouse cursor movement, can work together as a low-cost, scalable tool to help assess noncompliance risk. This paper contributes to theory by explaining how people who are noncompliant tend to provide more lenient answers to questions about what constitutes compliance and the consequences of noncompliance. In addition, we show how mouse-cursor deviation provides theoretical insight into the level of cognitive dissonance that users experience, and that users who are noncompliant show greater deviation on compliance and consequence questions.