A Design Framework and AI System for Affective Destination Image Generation to Influence Tourists’ Emotional Response

Affective destination images have received considerable attention from tourism marketing researchers as evidence suggests that affective components in destination images affect tourists’ emotional responses, which in turn influence their behavioural intentions toward the destination. Therefore, tourism practitioners seek solutions to influence the emotional effects of affective destination images for B2C communication. This paper presents a design science research project to develop an AI system to assist practitioners in generating affective destination images that potentially trigger the desired emotional responses of tourists. By leveraging knowledge and techniques from NeuroIS, this paper also proposes a framework of scientific experiments to assess how the generated affective destination images by the AI system affect tourists’ emotional experiences

Mediators of the relationship between self-control and pathological technology use: Negative affect and cognitive failures, but not self-efficacy

The widespread adoption of technologies such as smartphones, the Internet, and social media has been associated with the emergence of pathological technology use (e.g., Internet addiction). Prevalence rates of pathological technology use vary widely across age groups, cultures, and medium, although it is not uncommon for rates of mild to moderate pathological use to exceed 20%-30%. These relatively high prevalence rates have motivated researchers to identify the predictors of pathological use. The current study focuses on the relationship be- tween self-control and pathological technology use, and demonstrates that negative affect and cognitive failures, but not self-efficacy, partially mediate the association between self-control and pathological technology use. These findings re- veal some of the pathways by which poor self-control could lead to elevated levels of pathological technology use

Appreciating the Performance of Neuroscience Mining in NeuroIS research: A Case Study on Consumer's Product Perceptions in the Two UI Modes—Dark UI vs. Light UI

The goal of the current study was to provide information on the potential of neuroscience mining (NSM) for comprehending NeuroIS paradigms. NSM is an interdisciplinary field that combines neuroscience and business mining, which is the application of big data analytics, computational social science, and other fields to business problems. Therefore, NSM makes it possible to apply predictive models to NeuroIS datasets, such as machine learning and deep learning, to find intricate patterns that are hidden by conventional regression-based analysis. We predicted 28 individual EEG power spectra separated brainwave data using a Random Forest (RF) model. Next, we used NSM to precisely predict how consumers would perceive a product online, depending on whether a light or dark user interface (UI) mode was being used. The model was then used to extract more precise results that could not be obtained using more conventional linear-based analytical models using sensitivity analysis. The benefits of using NSM in NeuroIS research are as follows: (1) it can relieve the burden of the three-horned dilemma described by Runkel and McGrath; (2) it can enable more temporal data to be directly analyzed on the target variables; and (3) sensitivity analysis can be performed on a condition/individual basis, strengthening the rigor of findings by reducing sample bias that can be lost in grand averaging of data when analyzed with methods like GLM

Engaged Brains: A Course on Neuro-Information Systems

Neuro-marketing, neuro-economics, and now the field of neuro-information systems (neuro-IS) is growing, and our students want to know more about it all. This paper presents the examination of the learning-needs of new entrants to the field of neuro-IS. The resulting elective course is targeted at IS undergraduate majors interested in learning about the use of neurophysiological tools in organizational settings. The course is focused on the design aspects of brain-based computer interfaces for people with disabilities and the general use of neurophysiological tools to understand human mental states better. Students read seminal papers to gain a background in the latest brain-based technology and its application to various organizations. The course material focuses on the design and usability of systems, the psychological and cognitive states of users, and the evaluation of novel technology. Students demonstrate their understanding of key concepts by designing and conducting a related research study, analyzing a case in the field, or designing their own brain-based interface. This course was taught to forty-one undergraduate students in a face-to-face format and thirty-seven in an online class using active learning principles, and the course was met with highly positive reviews. Delivering a version of the class online did not have a noticeable impact on either student performance or course evaluations

Chronic Pain and Eye Movements: A NeuroIS Approach to Designing Smart Clinical Decision Support Systems

The pressing need for objective measures in evaluating chronic pain in research and practice highlights the role that neuro information systems (NeuroIS) research plays in designing smart clinical decision support systems. A first step in such a research agenda involves identifying practical stimuli-task paradigms that can reliably detect chronic pain from physiological measures such as eye movements. In this study, we propose and test a new stimuli-task paradigm. Our results show that our proposed stimuli-task paradigm can detect differences in the information-processing behavior of people with and without chronic pain. The results also show that our proposed stimuli-task paradigm can reliably predict self-reported subjective pain experience from eye movements. These findings provide support for our proposed stimuli- task paradigm. They also show that the eye-tracking variables that we selected to test our proposed paradigm are effective in capturing the impact of chronic pain on visual attention, suggesting that eye movements have the potential to serve as reliable biomarkers of chronic pain. Hence, our results support the potential for eye movements to aid in efforts to develop smart information systems that can detect the presence and/or the severity of chronic pain from an individual’s ocular behavior

Evaluating User Experience in Multisensory Meditative Virtual Reality: A Pilot Study

Virtual Reality (VR) is known for its ability to immerse users in a parallel universe. Accordingly, VR offers great potential for mindfulness therapy, especially in a post pandemic world. However, the extent to which our senses should be recruited to yield an optimal feeling of presence in the Virtual Environment (VE) remains unclear. This study investigates lived and perceived effects of adding auditory and motor components to VR experiences, through narration and head movements respectively. Twelve participants experienced four nature-based VR videos in a within-subjects research design. The study employed a mixed method approach of psychometric and neurophysiological measures. Results support a significant relationship between positive affect and presence. While statistical support was not obtained for the remaining relationships, this study provides a feasibility assessment of utilizing NeuroIS methods in evaluating immersive user experiences, along with qualitative insights that extend our understanding towards optimized VE designs

Themes in Information Security Research in the Information Systems Discipline: A Topic Modeling Approach

Information security continues to grow in importance in all aspects of society, and therefore evolves as a prevalent research area. The Information Systems (IS) discipline offers a unique perspective from which to move this stream of literature forward. Using a semi-automated thematic analysis approach based on the topic modeling technique, we review a broad range of information security literature to investigate how we might theorize about information security on a grander scale. Five themes resulted from our analysis: Software Security Decisions, Firm Security Strategy, Susceptibility, Information Security Policy Compliance, and Other Developing Themes. Implications of our findings and future research directions are discussed

IT-Related Time Poverty: Identifying Antecedents and Consequences of a Lack of Time Related to IT Use

Time poverty is the subjective perception of inadequate freely disposable time, which results in negative consequences for individuals. Although information systems (IS) research knows that information technology (IT) use change time perception, research is incomplete in explaining IT-related antecedents and consequences of time poverty. Because time is a scarce resource, individuals, organizations, and society have a responsibility to manage time to protect individuals from adverse consequences. We conduct a structured literature review to identify indications of how IT use influences time poverty and its adverse consequences. We identified 16 papers, which we analyzed with respect to different components of IT use and possible consequences of time poverty. Based on the data, we develop an overview of the antecedents and consequences of IT-related time poverty and a research agenda. We contribute to the research by introducing IT-related time poverty as a new IS construct and providing an in-depth research agenda

Detecting Flow Experiences in Cognitive Tasks - A Neurophysiological Approach

Das Flow-Erlebnis beschreibt einen Zustand vollständiger Aufgabenvertiefung und mühelosen Handelns, der mit Höchstleistungen, persönlichem Wachstum, sowie allgemeinem Wohlbefinden verbunden ist. Für Unternehmen stellen häufigere Flow-Erlebnisse der ArbeitnehmerInnen daher auch eine produktivitäts- und zufriedenheitsfördernde Basis dar. Vor allem da sich aktuell globale Phänomene wie die steigende Nachfrage nach Wissensarbeit und das niedrige Arbeitsengagement zuspitzen, können Unternehmen von einer Förderung von Flow profitieren. Die Unterstützung von Flow stellt allerdings aufgrund der Vielfalt von Arbeitnehmerfertigkeiten, -aufgaben, und -arbeitsplätzen eine komplexe Herausforderung dar. WissensarbeiterInnen stehen dynamischen Aufgaben gegenüber, die diverse Kompetenzen und die Kooperation mit anderen erfordern. Arbeitsplätze werden vielseitiger, indem die Grenzen zwischen ko-präsenten und virtuellen Interaktionen verschwinden. Diese Vielfalt bedeutet, dass eine solide Flow-Förderung nur durch personen-, aufgaben- und situationsunabhängige Ansätze erfolgen kann. Aus diesem Grund werden zunehmend die neurophysiologischen Grundlagen des Flow-Erlebens untersucht. Auf deren Basis könnten adaptive Neuro-Informationssysteme entwickelt werden, die mittels tragbarer Sensorik Flow kontinuierlich erkennen und fördern können. Diese Wissensbasis ist bislang jedoch nur spärlich und in stark fragmentierter Form vorhanden. Für das Individuum existieren lediglich konkurrierende Vorschläge, die noch nicht durch situations- und sensorübergreifende Studien konsolidiert wurden. Für Gruppen existiert noch fast keine Forschung zu neurophysiologischen Flow-Korrelaten, insbesondere keine im Kontext digital-mediierter Interaktionen. In dieser Dissertation werden genau diese Forschungslücken durch die situationsübergreifende Beobachtung von Flow mit tragbaren EKG und EEG Sensoren adressiert. Dabei werden zentrale Grenzen der experimentellen Flow-Forschung berücksichtigt, vor allem die Defizite etablierter Paradigmen zum kontrollierten Hervorrufen von Flow. Indem Erlebnisse in zwei kognitiven Aufgaben und mehreren Manipulationen (von Schwierigkeit, Natürlichkeit, Autonomie und sozialer Interaktion) variiert werden, wird untersucht, wie Flow intensiver hervorgerufen und wie das Erlebnis stabiler über Situationen hinweg beobachtet werden kann. Die Studienergebnisse deuten dabei insgesamt auf ein Flow-Muster von moderater physiologischer Aktivierung und mentaler Arbeitslast, von erhöhter, aufgabenorientierter Aufmerksamkeit und von affektiver Neutralität hin. Vor allem die EEG Daten zeigen ein diagnostisches Potenzial, schwächere von stärkeren Flow-Zuständen unterscheiden zu können, indem optimale und nicht-optimale Aufgabenschwierigkeiten (für Individuen und Gruppen) erkannt werden. Um das Flow-Erleben weiter zu fördern, werden geeignete Wege für zukünftige Forschung abschließend diskutiert