The Fault in Our Stars: Molecular Genetics and Information Technology Use

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Brown, Susan; Sias, Richard

Abstract: There is a growing interest in understanding the role of genetics in explaining ... Expand

Abstract: There is a growing interest in understanding the role of genetics in explaining heterogeneity in behaviors, including those related to information systems (IS). The majority of the recent genetics research focuses on searching the entire genome in genome-wide association studies (GWASs) to link DNA to human traits. The results of GWASs can be used on datasets to compute a measure of genetic propensity known as a polygenic score, or PGS. PGSs are widely viewed as the future of genetics research. We conducted an exploratory study, in the context of information technology (IT) use, to examine if the PGS approach can be used to better understand the role of genetics in IS research. Consistent with our hypotheses, genetic endowments associated with Educational Attainment and General Cognition positively predict technology use, and genetic endowments associated with Neuroticism, Depressive Symptoms, Myocardial Infarction, and Coronary Artery Disease negatively predict technology use more than half a century later (genetic endowments are established at conception and our sample consists of individuals aged 50 to 80). Many of the characteristics known to be associated with heterogeneity in IT use (e.g., trust, education) appear to be mediators linking PGSs to IT use. Nonetheless, a number of PGSs maintain meaningful direct effects.This appendix 26 provides additional background and details on molecular genetics, and some of the major issues surrounding the construction and use of GWASs and PGSs. For readers interested in learning more, we recommend Browne and Walden's (2020) Appendix A for additional details on molecular genetics and the structure of DNA, Duncan et al. (2019) for additional details on GWASs, Choi et al. (2020) for additional details on PGSs, and Freese (2018) and Harden and Koellinger (2020) for discussions on using PGSs in social science research. Collapse

Topics: information system use social media personality accounting video conference

Methods: survey mediation analysis descriptive statistic qualitative interview NeuroIS

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

2023 | HICSS | Citations: 0

Authors: Costello, F.; Kim, M.; Lee, K.

Abstract: The goal of the current study was to explore the potential of neuroscience mini ... Expand

Abstract: The goal of the current study was to explore 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 areas to business problems. NSM makes it possible to apply predictive models to NeuroIS datasets, such as machine learning and deep learning, to find intricate patterns hidden by conventional regression-based analysis. First, 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 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 threehorned 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. Collapse

Topics: user interface big data usability tablet artificial intelligence

Methods: electroencephalography NeuroIS computational algorithm sensitivity analysis machine learning

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

Cognitive load during planned and unplanned virtual shopping: Evidence from a neurophysiological perspective

2023 | International Journal of Information Management | Citations: 0

Authors: Kakaria, Shobhit; Saffari, Farzad; Z. Ramsøy, Thomas; Bigné, Enrique

Abstract: Rapid adoption of virtual-reality-assisted retail applications is inadvertently ... Expand

Abstract: Rapid adoption of virtual-reality-assisted retail applications is inadvertently reshaping consumer buying pat­ terns, making it crucial for businesses to enhance their shopping experience. This new scenario challenges marketers with unique hurdles in both the commercialization of products and in managing information cues derived via VR retailing. Therefore, this study examined consumers’ impulsive behavior and unplanned pur­ chases in a virtual retail store, using self-reports and electroencephalography. Borrowing assorted perspectives from retailing, virtual reality, and neuromarketing literature, we extended the stimulus-organism-response framework to evaluate how unplanned behavior evolves through conscious and unconscious measures. We found that consumers’ impulsiveness was significantly associated with their unplanned expenditure and the number of unplanned purchases. Using mediation analysis, we observed that flow experience during shopping partially mediated the relationship between the sense of presence and the desire to stay longer in a virtual shopping store. Desire to stay in the virtual store positively influenced store satisfaction, basket-size deviation, and budget deviation. Additionally, cognitive workload obtained via electroencephalogram revealed significant differences during both planned and unplanned purchases. These findings provide fresh opportunities for re­ tailers to leverage the disruptive potential of immersive and interactive virtual technology to transform consumer shopping experiences. Collapse

Topics: virtual reality decision making psychological ownership social media marketing management

Methods: electroencephalography self reported survey longitudinal research neurophysiology experiment

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

Ambivalence is Better than Indifference: Behavioral and Neurophysiological Assessment of Ambivalence in Online Environments

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Lakhiwal, Akshat; Bala, Hillol; Léger, Pierre-Majorique

Abstract: Information representations such as ratings and reviews play an important role ... Expand

Abstract: Information representations such as ratings and reviews play an important role in assisting users in making decisions in online environments. Prior information systems (IS) research has mostly focused on the role of extreme valence, i.e., the positivity/negativity of information, portrayed by such representations. Yet this bipolar approach discounts how the coexistence of positivity and negativity (i.e., ambivalence) or their absence (i.e., indifference) is formed and leads to distinct attentional processes and outcomes such as purchase decisions. We theorize how and why the valence of information projected through such representations may elicit mixed feelings and influence decision-making in online environments. We conducted four randomized controlled experiments, including an electroencephalography (EEG) study, to disentangle the influence of ambivalence and indifference on decision-making in an online shopping context. We found that ambivalence and indifference to online information distinctly influenced attention and purchase decisions relative to positivity and negativity. Our findings further suggest the inability of incumbent bipolar representations, such as the widely implemented star rating system, to capture the mixed feelings expressed in online content. We propose a bivariate intervention that overcomes the limitations of bipolar representations by not only discerning ambivalence from indifference but also amplifying purchase decisions for products with ambivalent information by at least 50%, compared to incumbent bipolar representations. Our findings advance ongoing research on the role of information valence in online environments and offer implications for practice. Collapse

Topics: decision making enterprise resource planning digital platform user experience user attitude

Methods: experiment theory development electroencephalography NeuroIS statistical power analysis

What Disrupts Flow in Office Work? The Impact of Frequency and Relevance of IT-Mediated Interruptions

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Nadj, Mario; Rissler, Raphael; Adam, Marc; Knierim, Michael; Li, Maximilian; Maedche, Alexander; Riedl, René

Abstract: Flow, the holistic sensation people experience when they act with total involve ... Expand

Abstract: Flow, the holistic sensation people experience when they act with total involvement, is a known driver for desired work outcomes like task performance. However, the increasing ubiquity of IT at work can disrupt employees' flow. Thus, the impact of IT-mediated interruptions on flow warrants more attention in research and practice. We conducted a NeuroIS laboratory experiment focusing on a typical office work task-an invoice matching task (i.e., matching customer payments to invoices). We manipulated interruption frequency (low, high) and content relevance (irrelevant, relevant) to study the impact of interruptions on self-reported flow, its dimensions, and high-frequency heart rate variability (HF-HRV; calculated from electrocardiography recordings) as a proxy for parasympathetic nervous system (PNS) activation. We found that content relevance moderated the relationship between interruption frequency and self-reported flow and that these results vary along flow dimensions. Content relevance also moderated the relationship between interruption frequency and PNS activation. Furthermore, selfreported flow was positively associated with both perceived and objective task performance, while PNS Collapse

Topics: task productivity cognitive absorption Python technostress information system use

Methods: electrocardiography NeuroIS multivariate analysis of variance experiment descriptive statistic

Theories: flow theory

Mind Wandering in Information Technology Use: Scale Development and Cross- Validation

2023 | ACM SIGMIS Database | Citations: 0

Authors: Oschinsky, Frederike Marie; Klesel, Michael; Niehaves, Bjoern

Abstract: Because our minds frequently drift away, an investigation into mind wandering wh ... Expand

Abstract: Because our minds frequently drift away, an investigation into mind wandering while using information technology (IT) is critical. Despite growing interest in mind-wandering episodes in various domains, the discipline of Information Systems (IS) still lacks a validated measurement instrument that can account for the technology-related facets of the phenomenon. Our work addresses this gap and presents the results of a comprehensively developed scale that is specifically designed for IS scenarios. Using existing literature and the results of a pilot study (N = 35), a field study (N = 364), and a cross-validation sample (N = 336), we developed a new instrument that allows mind wandering while using technology to be measured either as a state (MWS) or as a trait with two subtypes (MWT-D: deliberate, and MWT-S: spontaneous). Whereas MWS captures a momentary mental state or a sequence of mental states that arise relatively freely while using technology in a given moment, MWT-D and MWT-S capture either intentional or unintentional, internally focused thoughts in technology-related settings in everyday life. Our scale is well suited to support future research to investigate the effects of mind wandering in technology-related settings and to study its implications for IS-relevant dependent variables, such as task performance and creativity. Collapse

Topics: information system use smartphone social media anonymity mobile application

Methods: chi squared test survey field study data transformation psychometrics

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

Prejudiced against the Machine? Implicit Associations and the Transience of Algorithm Aversion

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Turel, Ofir; Kalhan, Shivam

Abstract: Algorithm aversion is an important and persistent issue that prevents harvestin ... Expand

Abstract: Algorithm aversion is an important and persistent issue that prevents harvesting the benefits of advancements in artificial intelligence. The literature thus far has provided explanations that primarily focus on conscious reflective processes. Here, we supplement this view by taking an unconscious perspective that can be highly informative. Building on theories of implicit prejudice, in a preregistered study, we suggest that people develop an implicit bias (i.e., prejudice) against artificial intelligence (AI) systems, as a different and threatening "species," the behavior of which is unknown. Like in other contexts of prejudice, we expected people to be guided by this implicit bias but try to override it. This leads to some willingness to rely on algorithmic advice (appreciation), which is reduced as a function of people's implicit prejudice against the machine. Next, building on the somatic marker hypothesis and the accessibility-diagnosticity perspective, we provide an explanation as to why aversion is ephemeral. As people learn about the performance of an algorithm, they depend less on primal implicit biases when deciding whether to rely on the AI's advice. Two studies (n1 = 675, n2 = 317) that use the implicit association test consistently support this view. Two additional studies (n3 = 255, n4 = 332) rule out alternative explanations and provide stronger support for our assertions. The findings ultimately suggest that moving the needle between aversion and appreciation depends initially on one's general unconscious bias against AI because there is insufficient information to override it. They further suggest that in later use stages, this shift depends on accessibility to diagnostic information about the AI's performance, which reduces the weight given to unconscious prejudice. Collapse

Topics: artificial intelligence decision making accounting intrinsic motivation explainable artificial intelligence

Methods: computational algorithm parametric test longitudinal research statistical hypothesis test analysis of covariance