Near-Infrared Spectroscopy for Bladder Monitoring: A Machine Learning Approach

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Fechner, Pascal; König, Fabian; Kratsch, Wolfgang; Lockl, Jannik; Röglinger, Maximilian

Abstract: Patients living with neurogenic bladder dysfunction can lose the sensation of th ... Expand

Abstract: Patients living with neurogenic bladder dysfunction can lose the sensation of their bladder filling. To avoid over-distension of the urinary bladder and prevent long-term damage to the urinary tract, the gold standard treatment is clean intermittent catheterization at predefined time intervals. However, the emptying schedule does not consider actual bladder volume, meaning that catheterization is performed more often than necessary, which can lead to complications such as urinary tract infections. Time-consuming catheterization also interferes with patients' daily routines and, in the case of an empty bladder, uses human and material resources unnecessarily. To enable individually tailored and volume-responsive bladder management, we design a model for the continuous monitoring of bladder volume. During our design science research process, we evaluate the model's applicability and usefulness through interviews with affected patients, prototyping, and application to a real-world in vivo dataset. The developed prototype predicts bladder volume based on relevant sensor data (i.e., near-infrared spectroscopy and acceleration) and the time elapsed since the previous micturition. Our comparison of several supervised state-of-the-art machine and deep learning models reveals that a long short-term memory network architecture achieves a mean absolute error of 116.7 ml that can improve bladder management for patients. Collapse

Topics: computer hardware open source mobile application user interface programming language

Methods: design artifact qualitative interview design process support vector machine computational algorithm

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

Assessing the Decision-Making Process in Human-Robot Collaboration Using a Lego-like EEG Headset

2023 | HICSS | Citations: 0

Authors: Tsao, C.; Chuang, H.; Tsao, T.; Tang, C.; Chang, Y.; Chu, C.; Sung, C.; Hsieh, C.; Lin, Y.; Chien, S.

Abstract: Human-robot collaboration (HRC) has become an emerging field, where the use of ... Expand

Abstract: Human-robot collaboration (HRC) has become an emerging field, where the use of a robotic agent has been shifted from a supportive machine to a decisionmaking collaborator. A variety of factors can influence the effectiveness of decision-making processes during HRC, including the system-related (e.g., robot capability) and human-related (e.g., individual knowledgeability) factors. As a variety of contextual factors can significantly impact the human-robot decision-making process in collaborative contexts, the present study adopts a Lego-like EEG headset to collect and examine human brain activities and utilizes multiple questionnaires to evaluate participants' cognitive perceptions toward the robot. A user study was conducted where two levels of robot capabilities (high vs. low) were manipulated to provide system recommendations. The participants were also identified into two groups based on their computational thinking (CT) ability. The EEG results revealed that different levels of CT abilities trigger different brainwaves, and the participants' trust calibration of the robot also varies the resultant brain activities. Collapse

Topics: robotic decision making extended reality problem solving task productivity

Methods: electroencephalography survey experiment experimental task functional magnetic resonance imaging

"Feel, Don't Think" Review of the Application of Neuroscience Methods for Conversational Agent Research

2022 | European Conference On Information Systems | Citations: 0

Authors: Greulich, R. Stefan; Brendel, Alfred Benedikt

Abstract: Conversational agents (CAs) equipped with human-like features (e.g., name, avat ... Expand

Abstract: Conversational agents (CAs) equipped with human-like features (e.g., name, avatar) have been reported to induce the perception of humanness and social presence in users, which can also increase other aspects of users' affection, cognition, and behavior. However, current research is primarily based on self-reported measurements, leaving the door open for errors related to the self-serving bias, socially desired responding, negativity bias and others. In this context, applying neuroscience methods (e.g., EEG or MRI) could provide a means to supplement current research. However, it is unclear to what extent such methods have already been applied and what future directions for their application might be. Against this background, we conducted a comprehensive and transdisciplinary review. Based on our sample of 37 articles, we find an increased interest in the topic after 2017, with neural signal and trust/decision-making as upcoming areas of research and five separate research clusters, describing current research trends. Collapse

Topics: database system decision making search engine social presence

Methods: neurophysiology literature sample cluster analysis time series analysis longitudinal research

Trust Me, I’m an Influencer! - A Comparison of Perceived Trust in Human and Virtual Influencers

2022 | European Conference On Information Systems | Citations: 0

Authors: Hofeditz, Lennart; Nissen, Anika; Schütte, Reinhard; Mirbabaie, Milad

Abstract: ... Expand

Abstract: Collapse

Topics: Instagram social presence social media online marketing marketing management

Methods: functional neuroimaging survey functional near infrared spectroscopy self reported survey survey design

Exploring the personalization-intrusiveness-intention framework to evaluate the effects of personalization in social media

2022 | International Journal of Information Management | Citations: 0

Authors: Lee, Jungwoo; Kim, Cheong; Lee, Kun Chang

Abstract: Personalization is prevalent in modern social media marketing strategies. Howeve ... Expand

Abstract: Personalization is prevalent in modern social media marketing strategies. However, the users of social media services may perceive that personalized content is intrusive. These two components may influence consumers’ purchase intention in social media personalization campaigns. Thus, this positivism empirical research proposes a theoretical framework named PI2 using the personalization-intrusiveness-intention, as well as a success mea­ surement based upon privacy calculus theory for effective social media personalization campaigns. Further, we investigated digital mnemonics’ potential influences with emotion as social information theory and different degrees of product involvement together with the model. Based upon the analysis of a total of 800 social media users, the results indicated that personalization positively affected but intrusiveness negatively affected the purchase intention. Furthermore, personalization significantly affected purchase intention through intrusiveness and the interaction of personalization with digital mnemonics negatively influenced consumers’ purchase intention on high involvement ads. In the post-hoc analysis, the moderating effect of users’ demographic vari­ ables was found to have some influence on the PI2 framework. The findings of this research provide theoretical and managerial implications for researchers and field practitioners in social media personalization. Collapse

Topics: usability personalization social media marketing management advertising management

Methods: survey partial least squares regression factor analysis regression analysis method post-hoc analysis

Theories: privacy calculus theory information theory technology acceptance model

On the stress potential of videoconferencing: definition and root causes of Zoom fatigue

2022 | Electronic Markets | Citations: 13

Authors: Riedl, René

Abstract: As a consequence of lockdowns due to the coronavirus disease (COVID-19) and the ... Expand

Abstract: As a consequence of lockdowns due to the coronavirus disease (COVID-19) and the resulting restricted social mobility, several billion people worldwide have recently had to replace physical face-to-face communication with computer-mediated interaction. Notably, the adoption rates of videoconferencing increased significantly in 2020, predominantly because videoconferencing resembles face-to-face interaction. Tools such as Zoom, Microsoft Teams, and Cisco Webex are used by hundreds of millions of people today. Videoconferencing may bring benefits (e.g., saving of travel costs, preservation of environment). However, prolonged and inappropriate use of videoconferencing may also have an enormous stress potential. A new phenomenon and term emerged, Zoom fatigue, a synonym for videoconference fatigue. This paper develops a definition for Zoom fatigue and presents a conceptual framework that explores the major root causes of videoconferencing fatigue and stress. The development of the framework draws upon media naturalness theory and its underlying theorizing is based on research published across various scientific fields, including the disciplines of both behavioral science and neuroscience. Based on this theoretical foundation, hypotheses are outlined. Moreover, implications for research and practice are discussed. Collapse

Topics: video conference technostress information overload information system use virtual teamwork

Methods: theory development literature sample structured literature research experiment functional near infrared spectroscopy

Theories: media naturalness theory

Chances and Limits of Community-Based Hate Speech Detection – Results from a Combined Behavioral-NeuroIS Study

2022 | International Conference on Information Systems | Citations: 0

Authors: Zimmermann-Janssen, Vita; Gier, Nadine

Abstract: Communication via social media is characterized by immediacy and anonymity, ena ... Expand

Abstract: Communication via social media is characterized by immediacy and anonymity, enabling free expression and sharing of opinions, but also the abuse of language in form of hate speech. Given the volume of online content, IS research offers approaches to efficiently detect hate speech. However, research and politics call for more independent, transparent, and social approaches to increase credibility and acceptance. In response, this two-part behavioral and neural study investigates flagging as a community-based solution to hate speech detection. By experimentally varying the displayed shares of flagging users and testing behavioral responses, results reveal opposing behavioral patterns as a function of the valuation of hate speech prevention. Moreover, by framing the display of the user community's flagging behavior as a sort of social normative information and hate speech prevention as a public good, the theoretical model might help explain (seemingly) conflicting results in social norm and public goods research. Collapse

Topics: public good social norm social media logistics management anonymity

Methods: functional near infrared spectroscopy survey parametric test experimental task ANOVA

Oh, What a Cognitive Relief! A NeuroIS Study on Visual Designs of Digital Signages

2021 | International Conference on Information Systems | Citations: 0

Authors: Nissen, Anika; Obermeier, Gabriele; Gier, Nadine; Auinger, Andreas

Abstract: Digital signages are the most diffused instore technologies with their effects ... Expand

Abstract: Digital signages are the most diffused instore technologies with their effects on perceived store atmospherics and behavioral outcomes relying heavily on their visual design and context. To further inform and understand the effects of visual design, this research in vestigates the effect of digital signage designs from the lens of FuzzyTrace Theory which differentiates between a verbatim and gistbased processing of (visual) information. The designs were embedded within a store environment and without this context to validate the design's effect in context. The results of our study using functional nearinfrared spec troscopy show activated brain areas in the medial prefrontal cortex (PFC) accompanied by a lateral PFC deactivation, which indicates cognitive relief and increased emotional processing for gistbased designs. In store context, the cognitive relief is no longer found, yet the emotional attribution was still found. These results provide several theoretical and practical implications for the visual design of digital signages. Collapse

Topics: behavioral intention systems design laptop computer electronic commerce website design

Methods: functional near infrared spectroscopy functional neuroimaging experiment computational algorithm experimental task

Theories: processing fluency theory

A Neural Investigation of eLoyalty on eCommerce Websites

2021 | International Conference on Information Systems | Citations: 1

Authors: Nissen, Anika; Gier, Nadine

Abstract: eLoyalty provides a major competitive advantage for online businesses, however ... Expand

Abstract: eLoyalty provides a major competitive advantage for online businesses, however it is difficult to define. To understand the underlying mechanisms of eloyalty, a neuroimaging study was conducted, identifying possible "neural" activations that point to antecedents of the users' eloyalty. Our results show that antecedents to eloyalty might be associated with an activation of the bilateral dorsolateral prefrontal cortex (PFC), and the right orbitofrontal/ventromedial cortex. Furthermore, a non-hypothesized deactivation of the left superior dorsomedial PFC was identified. This activation pattern can be related to increased pleasantness and reward attribution of the target website, as well as higher associated familiarity and user preference. Based on this it is discussed that antecedents to eloyalty may be associated with neural lock-in effects. These lock-in effects may be elicited through continuous, positive, and pleasant user experiences which ultimately lead to an emotional attachment that is "neurally encoded" into the user's brain. Collapse

Topics: website electronic commerce decision making digital service Netflix

Methods: functional near infrared spectroscopy functional neuroimaging experiment survey experimental design