Incorporating evolutionary adaptions into the cognitive fit model for data visualization

2023 | Decision Support Systems | Citations: 0

Authors: Bina, Saman; Kaskela, Timothy; Jones, Donald R.; Walden, Eric; Graue, William B.

Abstract: With the proliferation of data has arisen an array of information visualization ... Expand

Abstract: With the proliferation of data has arisen an array of information visualization tools to help humans convert data to information for better decision-making. This ever-expanding visualization toolset has outrun the development of theories of data visualization usage for decision support. In the current research, we contribute to the development of visualization theories in two ways. First, we incorporate our understanding of evolutionary adaptations into the cognitive fit theory to explain why 3-D surface graphs often outperform traditional 2-D graphs in various sensemaking tasks. Drawing on geon theory to interpret the 3-D advantage, we develop the concept of geonic/non-geonic information representations and tasks. We tested and found support for our hy­ potheses that geonic representations have a speed advantage for the tasks we studied, derived from the human evolved capacity for geographic orienteering, but accuracy depends on the match of representation and task, as geonic or non-geonic. Our experiment compared the geonic representation (3-D surface graph) to non-geonic planar representations (cluster bar, heat map, and line) in geonic (integration, proportion) and non-geonic atomic (acquisition, comparison) tasks. For our second contribution, we build on this result to expand the cognitive fit model to incorporate classic human information processes and evolutionary adaptations. We suggest that the cognitive fit model can expand to include new advances in neuroscience. We discuss the theoretical and practical implications of our experiment and our expanded model. Collapse

Topics: data visualization sensemaking decision making spreadsheet Python

Methods: experiment parametric test ANOVA exploratory data analysis descriptive statistic

Theories: cognitive fit theory

What makes a Tweet so sweet: The neurocognitive effects of hedonic information consumption

2023 | Information & Management | Citations: 0

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

Abstract: Hedonic information consumption (HIC) refers to the consumption of information f ... Expand

Abstract: Hedonic information consumption (HIC) refers to the consumption of information for non-task-oriented purposes such as entertainment or pleasure. With the explosive growth of information availability through online channels such as social media, HIC has become a common and growing phenomenon; however, most information systems research to date has focused on factors surrounding information consumption for utilitarian rather than hedonic purposes. In this paper, we take an initial step to inform theory in this area by applying a NeuroIS lens to explore HIC behavior. We postulate that information is more likely to be hedonically consumed when it is appraised as interesting, and that certain characteristics of the information involved in HIC, namely its novelty and valence, play a role in determining its interestingness. Moreover, we hypothesize that HIC is driven by the neurobiological reactions that take place in response to information novelty and valence. We report the results of a behavioral and an fMRI experiment designed to measure the relationship between these information dimensions and neu­ rocognitive responses in the context of a popular social media platform. Our results provide preliminary evidence for differential processing patterns related to novelty and valence that can inform ongoing theory building surrounding HIC behaviors. Collapse

Topics: Twitter social media social media site database system decision making

Methods: experiment functional magnetic resonance imaging theory development NeuroIS magnetic resonance imaging

Theories: theory of information information systems 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

DISTURBING DISTRACTIONS: INVESTIGATING THE IM-PACT OF DIGITAL AND NON-DIGITAL DISTRACTIONS ON TASK PERFORMANCE

2023 | European Conference On Information Systems | Citations: 0

Authors: Mølgaard, Nina; Hubert, Marco; Steinmann, Sascha; Riedl, René

Abstract: Connection norms have forced individuals to keep their smartphone within arm's ... Expand

Abstract: Connection norms have forced individuals to keep their smartphone within arm's length to be reachable anytime-anywhere. This has led to strong connection habits that, paired with the boundless nature of the smartphone, have increased the possibilities of being exposed to distracting (auditory) cues triggering smartphone related habits. In this study we investigate whether digital (sound) distractions were more distracting compared to non-digital (sound) distractions as a result of smartphones being highly prevalent in our society and how a local distraction effect might impact overall task performance. We found that digital distractions did have a local distraction effect, but these local distractions did not amount to any significant group differences in terms of overall task performance. Although, it was found that individuals exposed to digital distractions reported increased perceived mental effort, -task difficulty, -subjective distraction and reduced perceived attention paid to the task compared to the nondigital and control groups. Collapse

Topics: social media task productivity smartphone Facebook Messenger

Methods: experimental group experiment longitudinal research parametric test ANOVA

Protection Motivation Theory in Information Security Behavior Research: Reconsidering the Fundamentals

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

Authors: Siponen, Mikko; Rönkkö, Mikko; Fufan, Liu; Haag, Steffi; Laatikainen, Gabriella

Abstract: Scholars commonly use protection motivation theory (PMT) by Rogers to examine i ... Expand

Abstract: Scholars commonly use protection motivation theory (PMT) by Rogers to examine information systems (IS) security behaviors and behavioral intentions. A recent, influential paper by Boss, Galletta, Lowry, Moody, and Polak (2015; hereafter BGLMP) in MIS Quarterly outlines correct and incorrect uses of PMT in Information Security behavior research. In this paper, we review some of BGLMP's key recommendations, such as the claim that all IS behavior studies that apply PMT should always use the model of the full theory, contain and measure fear, and measure actual behaviors. We defend an interpretation of Rogers (1975Rogers ( , 1983) that differs from the interpretation that BGLMP propose. We present evidence that Rogers' PMT and the empirical evidence do not adequately support many of BGLMP's suggestions and that these suggestions contradict good scientific practices (e.g., restricting the use of the method of isolation) that the philosophy of science and the original literature on PMT uphold. As a result, if reviewers and editors continue to embrace these recommendations, they could hinder the progress of IS behavior research by not allowing isolation or the combination of different theoretical components. In contrast to BGLMP's paper, we argue that further PMT research can focus on isolated PMT components and combine them with other theories. Some of our ideas (e.g., isolation) are not PMT-specific and could be useful for IS research in general. In summary, we contest BGLMP's recommendations and offer revised recommendations in return. Collapse

Topics: malware data security behavioral intention self efficacy social influence

Methods: functional magnetic resonance imaging literature study experiment experimental group structural equation modeling

Theories: protection motivation theory technology threat avoidance theory

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

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

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

IRIS: Learning the Underlying Information of Scientific Research Interests Using Heterogeneous Network Representation

2022 | HICSS | Citations: 0

Authors: Feng, Zihan; Cui, Hongfei

Abstract: Understanding scientific research fields and finding potential relations between ... Expand

Abstract: Understanding scientific research fields and finding potential relations between seemingly distinct fields can help researchers rapidly grasp their most interested topics with expertises. In this study, we construct a heterogeneous network which contains authors, keywords, papers and institutions, and built an “Integrated Research Interest Space (IRIS)” which can represent both author and keyword nodes. Similar keywords in the sense of research interest and research manner can obvious aggregate together. Authors that are interested in different keywords distributed in different IRIS areas, with strongly associated with research objectives and methodologies of the keywords. The average similarities between authors and their real used keywords is significantly higher than that of randomly chosen author-keyword pairs. Based on these observations, we propose a simple algorithm which attempts to recommend potential interested keywords for researchers, and got meaningful results. Our study may also give useful hints for understanding research interests and discovering potential cross disciplines. Collapse

Topics: website

Methods: computational algorithm data transformation t-distributed stochastic neighbor embedding cluster analysis deep learning

"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