How AI-Based Systems Can Induce Reflections: The Case of AI-Augmented Diagnostic Work

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Abdel-Karim, Benjamin; Pfeuffer, Nicolas; Carl, K. Valerie; Hinz, Oliver

Abstract: This paper addresses a thus-far neglected dimension in human-artificial intelli ... Expand

Abstract: This paper addresses a thus-far neglected dimension in human-artificial intelligence (AI) augmentation: machine-induced reflections. By establishing a grounded theoretical-informed model of machine-induced reflection, we contribute to the ongoing discussion in information systems (IS) regarding AI and research on reflection theories. In our multistage study, physicians used a machine learning-based (ML) clinical decision support system (CDSS) to see if and how this interaction can stimulate reflective practice in the context of an X-ray diagnosis task. By analyzing verbal protocols, performance metrics, and survey data, we developed an integrative theoretical foundation to explain how ML-based systems can help stimulate reflective practice. Individuals engage in more critical or shallower modes depending on whether they perceive a conflict or agreement with these CDSS systems, which in turn leads to different levels of reflection depth. By uncovering the process of machine-induced reflections, we offer IS research a different perspective on how such AI-based systems can help individuals become more reflective, and consequently more effective, professionals. This perspective stands in stark contrast to the traditional, efficiency-focused view of MLbased decision support systems and also enriches theories on human-AI augmentation. Collapse

Topics: artificial intelligence decision making decision support system algorithmic transparency recommender system

Methods: cognitive walkthrough survey theory development qualitative interview Student's t-test

Theories: learning theory information systems theory theory of abduction

Two Sides of the Same Pillow: Unfolding the Relationship between Objective and Subjective Sleep Quality with Unsupervised Learning

2023 | International Conference on Information Systems | Citations: 0

Authors: Biedebach, Luka; Óskarsdóttir, María; Arnardottir, Erna; Islind, Anna Sigríður

Abstract: ... Expand

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Topics: mobile system smart watch mobile application smart wearable device healthcare data

Methods: descriptive statistic cluster analysis parametric test ANOVA longitudinal research

ICU Outcome Predictions Using Real-Time Signals with Wavelet-Transform-based Convolutional Neural Network

2023 | HICSS | Citations: 0

Authors: Jiang, Yiqun; Wang, Shaodong; Li, Qing; Zhang, Wenli

Abstract: Intensive care units (ICUs) serve patients with life-threatening conditions. The ... Expand

Abstract: Intensive care units (ICUs) serve patients with life-threatening conditions. The limited ICU resources cause severe economic and healthcare burdens worldwide. It is critical to conduct ICU outcome predictions at an early stage and promote efficient use of ICU resources. However, all the current prediction methods have limitations such as unsatisfactory accuracy and depending on resource-demanding laboratory tests or expert domain knowledge. In this research, we design a wavelet-transformed-based convolutional neural network, WTCNN, which only requires patients’ vital sign series and information at ICU admission for real-time ICU outcome predictions. The model is evaluated using a large real-world ICU database and outperforms state-of-art baselines on both ICU mortality and length-of-stay prediction tasks. We conduct LIME for model interpretation and prescriptive analysis. Our work provides an efficient tool for ICU outcome predictions, allowing healthcare providers to take action promptly on patients at risk and reduce the negative impacts on patient outcomes. Collapse

Topics: local interpretable model agnostic explanation database system missing data societal evaluation criteria healthcare data

Methods: machine learning longitudinal research time series analysis deep learning electrocardiography

Interpretable machine learning-based approach for customer segmentation for new product development from online product reviews

2023 | International Journal of Information Management | Citations: 0

Authors: Joung, Junegak; Kim, Harrison

Abstract: For new product development, previous segmentation methods based on demographic, ... Expand

Abstract: For new product development, previous segmentation methods based on demographic, psychographic, and purchase behavior information cannot identify a group of customers with unsatisfied needs. Moreover, seg­ mentation is limited to sales promotions in marketing. Although needs-based segmentation considering customer sentiments on product features can be conducted to develop a new product concept, it cannot identify com­ monalities among customers owing to their diverse preferences. Therefore, this paper proposes an interpretable machine learning-based approach for customer segmentation for new product development based on the importance of product features from online product reviews. The technical challenges of determining the importance of product features in each review are identifying and interpreting the nonlinear relations between satisfaction with product features and overall customer satisfaction. In this study, interpretable machine learning is used to identify these nonlinear relations with high performance and transparency. A case study on a wearable device is conducted to validate the proposed approach. Customer segmentation using the proposed approach based on importance is compared with that employing a previous approach based on sentiments. The results show that the proposed approach presents a higher clustering performance than the previous approach and offers opportunities to identify new product concepts. Collapse

Topics: online review shapley additive explanation customer satisfaction new product development Python

Methods: cluster analysis machine learning sentiment analysis case study word2vec

Theories: game theory

Embracing brain and behaviour: Designing programs of complementary neurophysiological and behavioural studies

2023 | Information Systems Journal | Citations: 3

Authors: Kirwan, C. Brock; Vance, Anthony; Jenkins, Jeffrey L.; Anderson, Bonnie Brinton

Abstract: NeuroIS—the methods and knowledge of neuroscience applied to the information sys ... Expand

Abstract: NeuroIS—the methods and knowledge of neuroscience applied to the information systems (IS) domain—has become an established research field within the IS discipline. A key advantage of NeuroIS is its ability to provide insights into human cognition beyond those obtained using behavioural techniques alone. Nevertheless, in neuroscience, there is renewed interest in examining behaviour together with neurophysiological methods to better inform our understanding of neural processes. In this research opinion article, we argue that in the field of NeuroIS, there is an opportunity for hybrid programs of study that combine neurophysiological and behavioural methods in a complementary manner. We outline four strategies for designing complementary neurophysiological and behavioural experiments in a research program: (1) observe the relationship between neural processes and behavioural change; (2) combine neurophysiological and behavioural methods to enhance internal, external, and ecological validity; (3) extend, rather than replicate, experiments based on theory; and (4) use neurophysiological and behavioural experiments together to evaluate IT artefact design. By applying these strategies, researchers can more effectively design programs using complementary neurophysiological and behavioural methods, which, in turn, can help to provide richer insights into the phenomena under study as well as accelerate the advancement of IS knowledge. Collapse

Topics: marketing management Google+ IT security threat personal computer enterprise resource planning

Methods: experiment NeuroIS neurophysiology oculometry functional magnetic resonance imaging

Disentangling Emotional and Cognitive Factors of Escalation of Commitment: Evidence for a Psychophysiological Link

2023 | European Conference On Information Systems | Citations: 0

Authors: Marx, Carolin; Uebernickel, Falk

Abstract: Escalation of commitment -the tendency to persist with failing courses of actio ... Expand

Abstract: Escalation of commitment -the tendency to persist with failing courses of action -can determine whether a distressed Information Systems (IS) project can be turned around. To disentangle the emotional and cognitive factors that give rise to escalation we conducted a between-subject randomized controlled laboratory experiment with 75 Master, MBA, and Ph.D. students, including data triangulation between neurophysiological and behavioral measures. This study successfully replicates the bias in the context of IS project distress, provides evidence for a psychophysiological link, supports the predictions on the role of negative and complex emotional states of self-justification theory over coping theory, and adds to a better understanding of how escalation tendency changes over time due to learning effects. Our findings contribute to enhancing decision-making in uncertain environments by using cognitive and emotional markers and thereby provide the foundation for developing neuro-adaptive de-escalation strategies. Collapse

Topics: decision making IT project artificial intelligence cloud computing decision support

Methods: regression analysis method simulation biometric measurement experiment descriptive statistic

Theories: self justification theory cognitive dissonance theory cognitive theory

Positively Fearful: Activating the Individual’s HERO Within to Explain Volitional Security Technology Adoption

2023 | Journal of the Association for Information Systems | Citations: 0

Authors: Mattson, Thomas; Aurigemma, Sal; Ren, Jie

Abstract: Regardless of what security professionals do to motivate personal users to adop ... Expand

Abstract: Regardless of what security professionals do to motivate personal users to adopt security technologies volitionally, the end result seems to be the same-low adoption rates. To increase these rates, we propose activating their positive psychological capital (PsyCap), which consists of hope, self-efficacy, resilience, and optimism (i.e., their "HERO within"). We propose that greater PsyCap toward a security technology is associated with greater adoption rates (and intentions thereof) because positivity increases motivation. We further posit that PsyCap both moderates and is moderated by other constructs. We suggest that personal users' conditioned fear from the security threat moderates the effect of PsyCap on adoption intentions because some fear is necessary to activate their positive PsyCap to form their behavioral intentions to adopt security technologies. We further hypothesize that PsyCap moderates the effect of adoption intentions on actual adoption rates because activating an individual's HERO within encourages individuals to exert the effort necessary to translate their intentions into actual adoption. Finally, we theorize that enhancing fear appeal messages with appeals to an individual's HERO has a greater effect on volitional adoption rates relative to messages without these PsyCap-related appeals. To support our hypotheses, we conducted two experiments using the volitional adoption of a password manager application and a two-factor authentication (2FA) service. We found differential support for our hypotheses across the two security technologies, which suggests technology characteristics might mitigate the impact of PsyCap on volitional adoption decisions. and underwent three revisions.Thomas Mattson researches a variety of information systems topics including behavioral information security, online networks of practice, technology adoption, and continued use of information systems. He has published in a variety of scholarly journals such as MIS Quarterly, Journal of the Association Sal Aurigemma researches employee information security policy compliance, small business information security practices, and end-user security behaviors. He has published his research in a variety of scholarly journals such as the Jie Ren researches the business impact of collective online behaviors, crowdsourcing, online reviews, social media, and behavioral information security. Her research has been published in many scholarly journals such as the European Collapse

Topics: multi-factor authentication behavioral intention self efficacy password electronic mail

Methods: experimental group experiment survey design chi squared test descriptive statistic

Towards Design Principles for Experimental Simulations in Virtual Reality -Learning from Driving Simulators

2023 | HICSS | Citations: 0

Authors: Menck, J.; Lechte, H.; Lembcke, T.; Brendel, A.; Kolbe, L.

Abstract: Experiments play an important role in Information Systems research. In this are ... Expand

Abstract: Experiments play an important role in Information Systems research. In this area, Virtual Reality (VR) technologies can serve as a tool for enabling and conducting research. e.g., to investigate human behavior in specific situations. A prime example is VR-supported driving simulators that allow researchers in the automotive domain to gather knowledge while reducing cost and complexity compared to field studies with real cars. We argue that the use of carefully designed VR-supported experiments might allow researchers to get deeper insights into human behavior. Thus, we derive design principles for VR Experiments as an artifact from the literature about VR-supported driving simulations that have been accepted as a useful tool for research in their domain. Collapse

Topics: virtual reality computer hardware user experience task productivity extended reality

Methods: design principle simulation experiment literature study design science

How are you Feeling? Inferring Emotions through Movements in the Metaverse

2023 | International Conference on Information Systems | Citations: 0

Authors: Menck, Jannes; Uschakov, Evgeni; Lechte, Henrik; Lembcke, Tim-Benjamin; Kolbe, Lutz M.

Abstract: Metaverses are immersive virtual worlds in which people interact as avatars. Th ... Expand

Abstract: Metaverses are immersive virtual worlds in which people interact as avatars. There is emerging interest in understanding how metaverse users behave and perceive activities and tasks. Our understanding of users' behavior within metaverses is limited. This study examines the role of emotions in the movement of individuals. We therefore implement a metaverse setting using virtual reality technology and development tools. In our study, we manipulated negative emotions and tracked the movements of our participants. We show how negative emotion influences movements in a metaverse setting. Based on a literature review, we select and calculate movement features to train a support vector machine. As our result, we present a novel way to infer the negative emotions of metaverse users which will help create more engaging and immersive experiences that cater to user's emotions and behaviors. Our study provides preliminary evidence for the potential utilization of movement data in the metaverse. Collapse

Topics: virtual reality privacy extended reality Python smartphone

Methods: experiment survey support vector machine machine learning computational algorithm

What Disrupts Flow in Office Work? The Impact of Frequency and Relevance of IT-Mediated Interruptions

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Nadj, Mario; Rissler, Raphael; Adam, Marc; Knierim, Michael; Li, Maximilian; Maedche, Alexander; Riedl, René

Abstract: Flow, the holistic sensation people experience when they act with total involve ... Expand

Abstract: Flow, the holistic sensation people experience when they act with total involvement, is a known driver for desired work outcomes like task performance. However, the increasing ubiquity of IT at work can disrupt employees' flow. Thus, the impact of IT-mediated interruptions on flow warrants more attention in research and practice. We conducted a NeuroIS laboratory experiment focusing on a typical office work task-an invoice matching task (i.e., matching customer payments to invoices). We manipulated interruption frequency (low, high) and content relevance (irrelevant, relevant) to study the impact of interruptions on self-reported flow, its dimensions, and high-frequency heart rate variability (HF-HRV; calculated from electrocardiography recordings) as a proxy for parasympathetic nervous system (PNS) activation. We found that content relevance moderated the relationship between interruption frequency and self-reported flow and that these results vary along flow dimensions. Content relevance also moderated the relationship between interruption frequency and PNS activation. Furthermore, selfreported flow was positively associated with both perceived and objective task performance, while PNS Collapse

Topics: task productivity cognitive absorption Python technostress information system use

Methods: electrocardiography NeuroIS multivariate analysis of variance experiment descriptive statistic

Theories: flow theory