Human-Computer Interaction in the Digital Economy

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    Introduction to the Minitrack on Human-Computer Interaction in the Digital Economy
    ( 2023-01-03) Jenkins, Jeffrey ; Schneider, Christoph ; Valacich, Joseph
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    Typing Fast versus Typing Slow: Using Typing Dynamics to Reveal Authentic and Imposter Users
    ( 2023-01-03) Kim, David ; Valacich, Joseph ; Jenkins, Jeff ; Kumar, Manasvi
    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).
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    Subject-Independent Detection of Yes/No Decisions Using EEG Recordings During Motor Imagery Tasks: A Novel Machine-Learning Approach with Fine-Graded EEG Spectrum
    ( 2023-01-03) Penava, Pascal ; Brozat, Marie-Louise ; Zimmermann, Yara ; Breitenbach, Johannes ; Ulrich, Patrick ; Buettner, Ricardo
    The classification of sensorimotor rhythms in electroencephalography signals can enable paralyzed individuals, for example, to make yes/no decisions. In practice, these approaches are hard to implement due to the variability of electroencephalography signals between and within subjects. Therefore, we report a novel and fast machine learning model, meeting the need for efficiency and reliability as well as low calibration and training time. Our model extracts finely graded frequency bands from motor imagery electroencephalography data by using power spectral density and training a random forest algorithm for classification. The goal was to create a non-invasive generalizable method by training the algorithm with subject-independent EEG data. We evaluate our approach using one of the currently largest publicly available electroencephalography datasets. With a balanced accuracy of 73.94%, our novel algorithm outperforms other state-of-the-art non-subject-dependent algorithms.
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    Designing for Digital Wellbeing on a Smartphone: Co-creation of Digital Nudges to Mitigate Instagram Overuse
    ( 2023-01-03) Purohit, Aditya Kumar ; Barev, Torben ; Schöbel, Sofia ; Janson, Andreas ; Holzer, Adrian
    The endless stream of social media newsfeeds and stories captivates users for hours on end, sometimes exceeding what users themselves consider unhealthy. However, reducing one's social media consumption has proven to be challenging. To address this issue, this study investigates how the co-creation of the digital feedback nudge can improve digital well-being without increasing privacy threats. To achieve this goal, a mixed method study is used through a two-week pre-post study design. Results demonstrate that co-creation significantly increased users' sense of agency, sense of accomplishment and perceived sense of privacy while reducing users' privacy concern. Furthermore, the feedback nudge allowed participants to significantly decrease their social media use.
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    Can a Negotiator Build a Tough Impression Without Chatting? —— Implicit Power and its Influence on Human-Computer Negotiation
    ( 2023-01-03) Liu, Yushan ; Vahidov, Rustam ; Saade, Raafat
    In this paper, we studied the influence of implicit power in an e-commerce setting where humans negotiated with computer agents. Implicit power is defined as a kind of perceived power gained indirectly through offer exchange. In much of the past research, power was always considered to be expressed directly through chat or natural language communications during negotiation. We suggest that there is another mode of expressing power other than chat: implicitly influencing. Specifically, we designed an experiment where several aspects of implicit power were studied: anchoring, agent profile image, and experiment subjects’ personality. In our experiment, the subjects negotiated the purchase of a laptop with computer agents acting as sellers. The result suggested that implicit power indeed influenced the negotiation result.
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    A Comparison of Paper Sketch and Interactive Wireframe by Eye Movements Analysis, Survey, and Interview
    ( 2023-01-03) Kieffer, Suzanne ; Vanderdonckt, Jean
    Eye movement-based analyses have been extensively performed on graphical user interface designs, mainly on high-fidelity prototypes such as coded prototypes. However, practitioners usually initiate the development life cycle with low-fidelity prototypes, such as mock-ups or sketches. Since little or no eye movement analysis has been performed on the latter, would eye tracking transpose its benefits from high- to low-fidelity prototypes and produce different results? To bridge this gap, we performed an eye movement-based analysis that compares gaze point indexes, gaze event types and durations, fixation, and saccade indexes produced by $N{=}8$ participants between two treatments, a paper prototype vs. a wireframe. The paper also reports a qualitative analysis based on the answers provided by these participants in a semi-directed interview and on a perceived usability questionnaire with 14 items. Due to its interactivity, the wireframe seems to foster a more exploratory approach to design (e.g., testing and navigating more extensively) than the paper prototype.
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    Exploring Future Personalization Opportunities in Technologies used by Older Adults with Mild to Moderate Dementia
    ( 2023-01-03) Wood, Rachel ; Dixon, Emma ; Elsayed-Ali, Salma ; Shokeen, Ekta ; Lazar, Amanda ; Lazar, Jonathan
    Technologies for aging are a growing market. These technologies have significant potential to support individuals whose cognitive changes can make everyday activities challenging. However, the adoption and use of these technologies by people with dementia (PwD) remain poor, indicating potential accessibility and usability issues. Such barriers limit PwD’s ability to contribute to the digital economy and fully engage with society. Personalization, which aligns technology with someone’s unique needs and preferences, may address these issues. We used mixed methods with ten people with mild to moderate dementia to explore how previous ways to personalize (i.e., Windows OS built-in features and settings) and newer personalization applications (i.e., Morphic) might reveal future opportunities for personalization features in technology for aging. This study contributes fifteen design considerations, which, if implemented, may increase the involvement of PwD in the digital economy and society.