311 research outputs found

    HICSS Panel Report on Cognitive Foreshadowing: Next Steps in Applying Neuroscience and Cognitive Science to Information Systems Research

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    The use of neurophysiological tools in the information systems domain has received increased attention over the last decade. The Hawaii International Conference on System Sciences has helped provide a home for rigorously exploring such work through related minitracks and symposia. This paper reports on a panel presented at the 49th HICSS conference held in 2016 during a symposium organized to help orient interested researchers to the usefulness of cognitive neuroscience in IS research. This paper first introduces the rise in the IS discipline for integrating the methodologies and tools of cognitive neuroscience. It then presents individual viewpoints from the varying panel members at the symposium as they addressed questions of longevity, applicability, and next steps for the neuroIS subdiscipline. The four panel members included Alan Dennis, Angelika Dimoka, Allen Lee, and Ofir Turel

    NeuroIS: Hype or Hope?

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    This panel discusses the opportunities and challenges of applying cognitive neuroscience theories, methods, and tools to inform IS theories, methods, and data (termed “NeuroIS”). Given the ability of cognitive neuroscience to localize the functionality of brain areas that underlie higher-order human processes using functional neuroimaging tools, many social scientists in economics, psychology, and marketing use such tools to derive many interesting insights by opening the “black box” of the brain. Recently in the IS discipline, there have been some attempts to explore the potential of cognitive neuroscience for IS research (e.g., Dimoka, Pavlou, and Davis 2007). The purpose of this panel is to explore the potential of cognitive neuroscience and functional neuroimaging tools for IS research, and consistent with the theme of this year’s ICIS, suggest whether and how NeuroIS may help IS academics conduct IT research that really matters. This panel will host an intellectual debate on the opportunities and challenges of employing cognitive neuroscience and functional neuroimaging tools in IS research. The panelists come from different disciplines (Marketing, IS, Neuroscience), theories (technology adoption, IS economics, IT productivity, design science), and methods (behavioral/organizational, economics, technical), and they will discuss how IS theories and methods in their respective areas can be complemented by cognitive neuroscience theories and neuroimaging data. They will also debate the potential of physiological data for IS research, the pros and cons of functional neuroimaging tools, and whether NeuroIS can help IS researchers do research that they could not do with other means. The panel will have a broad appeal to IS researchers who may be interested in the potential of cognitive neuroscience for IS research but they are concerned about the challenges associated with using neuroimaging tools. The panel will debate whether NeuroIS can help IS researchers learn more than they already know, and whether, how, and when cognitive neuroscience will prove beneficial for IS research. The panel will also debate whether and how NeuroIS can contribute to IS research, whether and how the IS field can benefit by cognitive neuroscience theories, and what research questions could arise from using neuroimaging tools in IS research. The panel’s ultimate goal is to gauge whether NeuroIS is “hype or hope,” aiming to conclude whether NeuroIS could provide valuable opportunities for IS research, or whether the challenges associated with neuroimaging tools will impede their wide usage

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

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    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

    Towards a NeuroIS Research Methodology: Intensifying the Discussion on Methods, Tools, and Measurement

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    The genesis of the Neuro-Information Systems (NeuroIS) field took place in 2007. Since then, a considerable number of IS scholars and academics from related disciplines have started to use theories, methods, and tools from neuroscience and psychophysiology to better understand human cognition, emotion, and behavior in IS contexts, and to develop neuro-adaptive information systems (i.e., systems that recognize the physiological state of the user and that adapt, based on that information, in real-time). However, because the NeuroIS field is still in a nascent stage, IS scholars need to become familiar with the methods, tools, and measurements that are used in neuroscience and psychophysiology. Against the background of the increased importance of methodological discussions in the NeuroIS field, the Journal of the Association for Information Systems published a special issue call for papers entitled “Methods, tools, and measurement in NeuroIS research” in 2012. We, the special issue’s guest editors, accepted three papers after a stringent review process, which appear in this special issue. In addition to these three papers, we hope to intensify the discussion on NeuroIS research methodology, and to this end we present the current paper. Importantly, our observations during the review process (particularly with respect to methodology) and our own reading of the literature and the scientific discourse during conferences served as input for this paper. Specifically, we argue that six factors, among others that will become evident in future discussions, are critical for a rigorous NeuroIS research methodology; namely, reliability, validity, sensitivity, diagnosticity, objectivity, and intrusiveness of a measurement instrument. NeuroIS researchers—independent from whether their role is editor, reviewer, or author—should carefully give thought to these factors. We hope that the discussion in this paper instigates future contributions to a growing understanding towards a NeuroIS research methodology

    Humans versus Agents: Competition in Financial Markets of the 21st Century

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    Information systems have revolutionized the nature of markets. Traditionally, markets inherently comprised the strategic interaction of human traders only. Nowadays, however, automated trading agents are responsible for at least 60% of the US trading volume on financial stock markets. In this respect, financial markets of the 21st century are different to markets of previous centuries. Fuelled by discussions on their possible risks, there is a need for research on the effects of automated trading agents on market efficiency and on human traders. In order to systematically investigate these issues, we introduce a market framework for human-computer interaction. This framework is then applied in a case study on a financial market scenario. In particular, we plan to conduct a NeuroIS experiment in which we analyze overall market efficiency as well as the trading behavior and emotional responses of human traders when they interact with computerized trading agents

    Brownie: A Platform for Conducting NeuroIS Experiments

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    In the NeuroIS field, experimental software needs to simultaneously present experimental stimuli to participants while recording, analyzing, or displaying neurophysiological measures. For example, a researcher might record a user’s heart beat (neurophysiological measure) as the user interacts with an e-commerce website (stimulus) to track changes in user arousal or show a user’s changing arousal levels during an exciting game. In this paper, we identify requirements for a NeuroIS experimental platform that we call Brownie and present its architecture and functionality. We then evaluate Brownie via a literature review and a case study that demonstrates Brownie’s capability to meet the requirements in a complex research context. We also verify Brownie’s usability via a quantitative study with prospective experimenters who implemented a test experiment in Brownie and an alternative software. We summarize the salient features of Brownie as follows: 1) it integrates neurophysiological measurements, 2) it incorporates real-time processing of neurophysiological data, 3) it facilitates research on individual and group behavior in the lab, 4) it offers a large variety of options for presenting experimental stimuli, and 5) it is open source and easily extensible with open source libraries. In summary, we conclude that Brownie is innovative in its potential to reduce barriers for IS researchers by fostering replicability and research collaboration and to support NeuroIS and interdisciplinary research in cognate areas, such as management, economics, or human-computer interaction

    The dual pathway to information avoidance in information systems use

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    This article develops an explanatory model of information avoidance behavior from extant theory and examines its hypotheses using psychophysiological methods. It integrates existing but partially conflicting explanations into a coherent positivist model based on Coping Theory. The existence of two distinct but interlinked causal pathways to information avoidance will be outlined. Both pathways are cause by defects in the information quality. The first pathway is grounded on being threatened by the information’s inconsistency. The second pathway is based on being distressed by the information’s complexity. Due to the involvement of cognition as well as affect, the usefulness of traditional measurement methods alone is deemed to be limited. Thus, we will draw upon recent advances from NeuroIS research in order to integrate psychophysiological measures into an extended, triangulated measurement protocol. This article intends to contribute to this special issue in three ways. First, it shapes a theoretical model for studying information avoidance which has received little attention in IS research. Second, it exemplifies the derivation and instantiation of a NeuroIS measurement model and the selection of appropriate NeuroIS methods for scrutinizing the theoretical information avoidance model. Third, based on the evidence of an experiment, it provides guidelines for how to conduct eye-tracking, pupillometry, and facial electromyography measurements as well as how to subsequently derive meaning from the initial data collected

    Understanding and Supporting Decision-Making in Electronic Auctions: A NeuroIS Approach

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    Making use of the potential of NeuroIS, I apply a NeuroIS approach in this thesis to further the understanding of decision-making and to analyze the opportunities for NeuroIS in decision-support, both in electronic auctions

    Emotions and cognitive workload in economic decision processes - A NeuroIS Approach

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    The influence of cognitive and emotions on decision processes have been recently highlighted. Emotions interplay with the process of cognition, and determine decision processes. In this work, the role of external and internal influences on economic decision processes are studied. A NeuroIS method is applied for measuring emotions and cognitive workload. The lack of a suitable experimental platform for performing NeuroIS studies was recognized and the platform Brownie was developed and evaluated

    NeuroIS—Alternative or Complement to Existing Methods? Illustrating the Holistic Effects of Neuroscience and Self-Reported Data in the Context of Technostress Research

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    Recent research has made a strong case for the importance of NeuroIS methods for IS research. It has suggested that NeuroIS contributes to an improved explanation and prediction of IS phenomena. Yet, such research is unclear on the source of this improvement; while some studies indicate that NeuroIS constitutes an alternative to psychometrics, implying that the two methods assess the same dimension of an underlying IS construct, other studies indicate that NeuroIS constitutes a complement to psychometrics, implying that the two methods assess different dimensions of an IS construct. To clarify the role of NeuroIS in IS research and its contribution to IS research, in this study, we examine whether NeuroIS and psychometrics/psychological methods constitute alternatives or complements. We conduct this examination in the context of technostress, an emerging IS phenomenon to which both methods are relevant. We use the triangulation approach to explore the relationship between physiological and psychological/self-reported data. Using this approach, we argue that both kinds of data tap into different aspects of technostress and that, together, they can yield a more complete or holistic understanding of the impact of technostress on a theoretically-related outcome, rendering them complements. Then, we test this proposition empirically by probing the correlation between a psychological and a physiological measure of technostress in combination with an examination of their incremental validity in explaining performance on a computer-based task. The results show that the physiological stress measure (salivary alpha-amylase) explains and predicts variance in performance on the computer-based task over and above the prediction afforded by the self-reported stress measure. We conclude that NeuroIS is a critical complement to IS research
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