I SEE NO FUN IN ENTERPRISE SYSTEMS: AN EXPLORATORY STUDY ON THE FIRST IMPRESSION USABILITY AND USER EXPERIENCE

2023 | European Conference On Information Systems | Citations: 0

Authors: Nissen, Anika; Wienand, Mareen; Schütte, Reinhard

Abstract: Technology acceptance is crucial, if newly implemented enterprise systems (ES) ... Expand

Abstract: Technology acceptance is crucial, if newly implemented enterprise systems (ES) in a company are to succeed. This is often addressed by end-user training during the implementation project. Perceived enjoyment and positive user experience (UX) have gained significant importance as technology acceptance factors. Yet, research on the design of such trainings is scarce, and literature with focus on perceived UX of ES even more so. This is in contrast to findings from other contexts which show that perceived UX may heavily impact user attitudes and learning motivation. As a first endavour in this direction, this paper presents an exploratory pre-study on first impressions of main operating ES with regard to expected usability and UX. Results show that ES are rated low, especially when compared to a universal UX benchmark. We discuss how more positive first impressions may positively impact motivation to learn the system, which will be investigated in a follow-up study. Collapse

Topics: usability website Microsoft Dynamics Oracle database NetSuite

Methods: survey functional neuroimaging survey design statistical hypothesis test structural equation modeling

Artificial Intelligence for Sustainability—A Systematic Review of Information Systems Literature

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

Authors: Schoormann, Thorsten; Strobel, Gero; Möller, Frederik; Petrik, Dimitri; Zschech, Patrick

Abstract: The booming adoption of Artificial Intelligence (AI) likewise poses benefits an ... Expand

Abstract: The booming adoption of Artificial Intelligence (AI) likewise poses benefits and challenges. In this paper, we particularly focus on the bright side of AI and its promising potential to face our society's grand challenges. Given this potential, different studies have already conducted valuable work by conceptualizing specific facets of AI and sustainability, including reviews on AI and Information Systems (IS) research or AI and business values. Nonetheless, there is still little holistic knowledge at the intersection of IS, AI, and sustainability. This is problematic because the IS discipline, with its socio-technical nature, has the ability to integrate perspectives beyond the currently dominant technological one as well as can advance both theory and the development of purposeful artifacts. To bridge this gap, we disclose how IS research currently makes use of AI to boost sustainable development. Based on a systematically collected corpus of 95 articles, we examine sustainability goals, data inputs, technologies and algorithms, and evaluation approaches that define the current state of the art within the IS discipline. This comprehensive overview enables us to make more informed investments (e.g., policy and practice) as well as to discuss blind spots and possible directions for future research. Collapse

Topics: artificial intelligence IS discipline climate change healthcare data consumer behavior

Methods: computational algorithm literature sample longitudinal research structured literature research regression analysis method

Examining the Low- Resolution Electromagnetic Tomography Technique for EEG Brain Mapping: Towards Methodological Advancement for IS Research

2023 | ACM SIGMIS Database | Citations: 0

Authors: Tejay, Gurvirender P.; Mohammed, Zareef A.

Abstract: NeuroIS presents a new opportunity for information systems research. Often used ... Expand

Abstract: NeuroIS presents a new opportunity for information systems research. Often used neuroscience techniques include brain mapping with the functional magnetic resonance imaging (fMRI) device or eventrelated potential time-domain studies with the electroencephalogram (EEG). The critics of EEG consider the poor spatial resolution as justification for EEG's inadequacy to brain mapping studies. However, the low-resolution electromagnetic tomography (LORETA) technique provides strong estimation parameters allowing EEG to perform brain mapping. This paper presents EEG (with lower number of channels) and LORETA techniques as an effective approach for exploratory investigation specially when researchers are constrained with lack of resources (specially at significantly lower costs). We demonstrate the effectiveness of EEG using sLORETA with respect to fMRI as proof-of-concept approach to study IS phenomenon. The results of such studies can serve as a preliminary step for further analysis with the use of more sophisticated neuroscience devices. This can enhance IS research by taking advantage of both high temporal and spatial resolution leading to reduced estimation errors of neural activity and stronger basis for correlating neural activity and specific tasks. We also present a set of guidelines for using the LORETA family of techniques in IS research. Collapse

Topics: digital distribution mobile application market corporate social responsibility usability

Methods: electroencephalography functional magnetic resonance imaging design artifact NeuroIS Student's t-test

Actively open-minded thinking is key to combating fake news: A multimethod study

2023 | Information & Management | Citations: 0

Authors: Mirhoseini, Mahdi; Early, Spencer; El Shamy, Nour; Hassanein, Khaled

Abstract: The fake news phenomenon has exposed the vulnerability of individuals and societ ... Expand

Abstract: The fake news phenomenon has exposed the vulnerability of individuals and societies to information manipu­ lation in social media. We conducted two studies to understand why people believe in fake news and propose a simple IT intervention method that can help in detecting disinformation. In Study 1, we designed a laboratory experiment using behavioral and neurophysiological tools to test two competing theories in the disinformation literature. Both behavioral and neurophysiological evidence support the classical reasoning account hypotheses and reject the motivated reasoning predictions, suggesting that the lack of actively open-minded thinking (AOT) is linked to the belief in fake news. An intervention method was designed (i.e., performance feedback) that reduces individuals’ overconfidence in their ability to detect fake news and encourages more analytical thinking. In Study 2, we conducted an online survey presenting participants with their performance feedback halfway through the survey. The results show that the intervention increased participants’ performance by 14%. Our study contributes to the research on fake news by providing behavioral and neurophysiological evidence in support of the classical reasoning account. It also offers a simple and practical method that increases users’ ability to detect fake news. Collapse

Topics: fake news social media disinformation Facebook decision making

Methods: electroencephalography survey experiment longitudinal research parametric test

Theories: feedback intervention theory

Ripples of Change – An AI Job Crafting Model for Human-in-Control

2022 | HICSS | Citations: 0

Authors: Afiouni, Rania; Pinsonneault, Alain

Abstract: Introducing a new Artificial Intelligence (AI) system disrupts workers’ sense of ... Expand

Abstract: Introducing a new Artificial Intelligence (AI) system disrupts workers’ sense of control. To restore it, individual workers are likely to engage in selfinitiated changes to their jobs. We build on job crafting theory and extend it to propose a theoretical model explaining the ripple effect of changes from tasks to skills, relationships, and finally job cognition. We introduce the concept of human-in-control (one’s perception of their ability to deliver desired work outcomes in a work context involving AI) as the goal of the job crafting process. Our work provides a novel and important perspective on adaptation to AI through job transformation. As such, it opens numerous avenues for research in this nascent stream. Collapse

Topics: artificial intelligence information system use technology adoption change management human resource management

Methods: theory development functional neuroimaging

Theories: job crafting theory

Information Filtering in Electronic Networks of Practice: An fMRI Investigation of Expectation (Dis)confirmation

2022 | Journal of the Association for Information Systems | Citations: 0

Authors: Fadel, Kelly; Meservy, Thomas; Kirwan, C. Brock

Abstract: Online forums sponsored by electronic networks of practice (ENPs) have become a ... Expand

Abstract: Online forums sponsored by electronic networks of practice (ENPs) have become an important platform for technology-mediated knowledge exchange, yet relatively little is known about how ENP participants filter and evaluate the information they encounter on these forums. This study integrates perspectives from expectation confirmation theory, prospect theory, and neuroscience research to explore how ENP forum filtering judgments are influenced when expectations formed on the basis of contextual cues are confirmed or disconfirmed by the examination of solution quality. We summarize six different models of expectation confirmation explored in previous IS literature and report the results of a neuroimaging experiment using functional MRI (fMRI) that paired both positive and negative contextual cues with high-and low-quality solutions on a mock ENP forum interface. Results show that evaluation judgments are strongest in conditions where initial contextual cue judgments are confirmed by examination of solution quality except when the perceived expectation-experience gap is large, providing evidence for an assimilation-contrast model of expectation confirmation. We also found neural activation differences for expectation confirmation vs. disconfirmation and, consistent with prospect theory, differences in filtering behaviors with respect to unexpected gains vs. unexpected losses. has also been presented and recognized at several international information systems conferences.Thomas O. Meservy is an associate professor of information systems at Brigham Young University. He earned his PhD from the University of Arizona in 2007. He has three main research streams including human decision-making in online forums, automated deception detection, and large group collaboration. His research has appeared in Management Information Systems Quarterly, Information Systems Research, Journal of Management Information Systems, and other high-quality outlets and conferences. He enjoys teaching. He also maintains ties to industry doing consulting and training related to software development, testing, and cybersecurity with the Department of Homeland Security, the Department of Defense, and Fortune 100 and Fortune 500 companies.C. Brock Kirwan is a professor of psychology and neuroscience and director of the MRI Research Facility at Brigham Young University. He leads the Memory and Decision Making Lab, which studies memory-dependent decision making. His collaborative research with the NeuroSecurity Lab using neuroscience methods applied to computer security problems has been funded by industry and the National Science Foundation. He has been published in Science, Collapse

Topics: software developer information exchange technology adoption database system Java

Methods: functional magnetic resonance imaging experiment magnetic resonance imaging statistical hypothesis test experimental task

Theories: prospect theory expectation confirmation theory dual-system theory information systems theory

Towards a Theory-Based Evaluation of Explainable Predictions in Healthcare

2022 | International Conference on Information Systems | Citations: 0

Authors: Ghanvatkar, Suparna; Rajan, Vaibhav

Abstract: Modern Artificial Intelligence (AI) models offer high predictive accuracy but o ... Expand

Abstract: Modern Artificial Intelligence (AI) models offer high predictive accuracy but often lack interpretability with respect to reasons for predictions. Explanations for predictions are usually necessary in making high-stakes clinical decisions. Hence, many Explainable AI (XAI) techniques have been designed to generate explanations for predictions from blackbox models. However, there are no rigorous metrics to evaluate these explanations, especially with respect to their usefulness to clinicians. We develop a principled method to evaluate explanations by drawing on theories from social science and accounting for specific requirements of the clinical context. As a case study, we use our metric to evaluate explanations generated by two popular XAI algorithms in the task of predicting the onset of Alzheimer's disease using genetic data. Our preliminary findings are promising and illustrate the versatility and utility of our metric. Our work contributes to the practical and theoretical development of XAI techniques and Clinical Decision Support Systems. Collapse

Topics: explainable artificial intelligence local interpretable model agnostic explanation knowledge graph application programming interface health information system

Methods: computational algorithm machine learning case study artificial neural network functional neuroimaging

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

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

Early Diagnosis of Mild Cognitive Impairment with 2-Dimensional Convolutional Neural Network Classification of Magnetic Resonance Images

2021 | HICSS | Citations: 0

Authors: Angelopoulos, Spyros

Abstract: We motivate and implement an Artificial Intelligence (AI) Computer Aided Diagnos ... Expand

Abstract: We motivate and implement an Artificial Intelligence (AI) Computer Aided Diagnosis (CAD) framework, to assist clinicians in the early diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD). Our framework is based on a Convolutional Neural Network (CNN) trained and tested on functional Magnetic Resonance Images datasets. We contribute to the literature on AI-CAD frameworks for AD by using a 2D CNN for early diagnosis of MCI. Contrary to current efforts, we do not attempt to provide an AI-CAD framework that will replace clinicians, but one that can work in synergy with them. Our framework is cheaper and faster as it relies on small datasets without the need of high-performance computing infrastructures. Our work contributes to the literature on digital transformation of healthcare, health Information Systems, and NeuroIS, while it opens novel avenues for further research on the topic. Collapse

Topics: artificial intelligence digital transformation health information system Python cloud computing

Methods: magnetic resonance imaging NeuroIS functional neuroimaging Keras machine learning