FREDS Research Publications

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    A machine learning approach for diagnostic and prognostic predictions, key risk factors and interactions
    (Springer, 2024) Nasir, Murtaza; Summerfield, Nichalin S.; Carreiro, Stephanie; Berlowitz, Dan; Oztekin, Asil
    Machine learning (ML) has the potential to revolutionize healthcare, allowing healthcare providers to improve patient-care planning, resource planning and utilization. Furthermore, identifying key-risk-factors and interaction-effects can help service-providers and decision-makers to institute better policies and procedures. This study used COVID-19 electronic health record (EHR) data to predict five crucial outcomes: positive-test, ventilation, death, hospitalization days, and ICU days. Our models achieved high accuracy and precision, with AUC values of 91.6%, 99.1%, and 97.5% for the first three outcomes, and MAE of 0.752 and 0.257 days for the last two outcomes. We also identified interaction effects, such as high bicarbonate in arterial blood being associated with longer hospitalization in middle-aged patients. Our models are embedded in a prototype of an online decision support tool that can be used by healthcare providers to make more informed decisions. © The Author(s) 2024.
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    Detecting deception in computer-mediated communication: The role of popularity information across media types
    (Emerald Publishing, 2024-01) Mirsadikov, Akmal; Vedadi, Ali; Marett, Kent
    Purpose: With the widespread use of online communications, users are extremely vulnerable to a myriad of deception attempts. This study aims to extend the literature on deception in computer-mediated communication by investigating whether the manner in which popularity information (PI) is presented and media richness affects users' judgments. Design/methodology/approach: This study developed a randomized, within and 2 - 3 between-subject experimental design. This study analyzed the main effects of PI and media richness on the imitation magnitude of veracity judges and the effect of the interaction between PI and media richness on the imitation magnitude of veracity judges. Findings: The manner in which PI is presented to people affects their tendency to imitate others. Media richness also has a main effect; text-only messages resulted in greater imitation magnitude than those viewed in full audiovisual format. The findings showed an interaction effect between PI and media richness. Originality/value: The findings of this study contribute to the information systems literature by introducing the notion of herd behavior to judgments of truthfulness and deception. Also, the medium over which PI was presented significantly impacted the magnitude of imitation tendency: PI delivered through text-only medium led to a greater extent of imitation than when delivered in full audiovisual format. This suggests that media richness alters the degree of imitating others' decisions such that the leaner the medium, the greater the expected extent of imitation.
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    Calibration training for improving probabilistic judgments using an interactive app
    (John Wiley and Sons Inc, 2023-12) Gruetzemacher, Ross; Lee, Kang Bok; Paradice, David
    We describe an exploratory study examining the effectiveness of an interactive app and a novel training process for improving calibration and reducing overconfidence in probabilistic judgments. We evaluated the training used in the app by conducting an American college football forecasting tournament involving 153 business school students making 52 forecasts over 11 weeks. A coarsened exact matching analysis found statistical evidence that, in under 30?min, the more challenging training was able to modestly reduce overconfidence, improve calibration and improve the accuracy of probabilistic judgments (measured by the Brier score). The experimental results also suggest that the generic training can generalize across domains and that effective calibration training is possible without expert facilitators or pedagogical training materials. Although no previous studies have reported similar results, due to the modest effect, we conclude that these results should only be interpreted as a proof of concept and that further evaluation and validation of mechanisms of the app's effect is necessary.
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    How job resources can shape perspectives that lead to better performance: A remote worker field study
    (Emerald Publishing, 2023-12) Keeler, Justin B.; Scuderi, Noelle F.; Brock Baskin, Meagan E.; Jordan, Patricia C.; Meade, Laura M.
    Purpose: The purpose of this study is to investigate the complexity of how demands and stress are mitigated to enhance employee performance in remote working arrangements. Design/methodology/approach: A time-lagged snowball sample of 223 full-time remote working adults in the United States participated in an online survey. Data were analyzed using R 4.0.2 and structural equation modeling. Findings: Results suggest remote job resources involving organizational trust and work flexibility increase performance via serial mediation when considering information communication technology (ICT) demands and work-life interference (WLI). The findings provide insights into counterbalancing the negative aspects of specific demands and stress in remote work arrangements. Practical implications: This study provides insights for managers to understand how basic job resources may shape perspectives on demands and WLI to impact performance. Specific to remote working arrangements, establishing trust with the employees and promoting accountability with their work flexibility can play an important part in people and their performance. Originality/value: This study contributes theoretically to the literature by evidencing how components of the E-Work Life (EWL) scale can be used with greater versatility beyond the original composite measurement because of the job-demand resource (JD-R) framework and conservation of resources theory (COR). This study answers several calls by research to investigate how ICT demands and WLI play a complex role in work performance.
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    Influence of Media Capabilities on Trust in the Sharing Economy
    (Routledge, 2023-08) Harrison, Andrew; Mirsadikov, Akmal; Luu, Truong
    Media capabilities influence consumers' trust in online exchanges. However, in the sharing economy, where consumers interact with service providers through a platform, conventional models of trust must be revisited. Our research identifies how media synchronicity and anonymity influence the relative importance of institution-based trust in sharing economy exchanges. We collected data from 248 ride-hailing customers and 288 cryptocurrency users to test a moderated mediation model of trust. We find that in the sharing economy media synchronicity and anonymity lead customers to develop trust toward service providers directly and undermine the impact of institutional trust mechanisms. This indicates that in sharing economy exchanges, trust can be built directly with the service provider, or alternatively, indirectly through the platform. Consequently, organizations in the sharing economy can strategically design their systems to engender trust by choosing between (1) emphasizing the platform's reputation or (2) encouraging direct communication between the consumer and service providers.