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

This is the final version. Available from Association for Information Systems via the DOI in this recordRecent 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

Precision is in the Eye of the Beholder: Application of Eye Fixation-Related Potentials to Information Systems Research

This is the final version. Available from Association for Information Systems via the DOI in this recordThis paper introduces the eye-fixation related potential (EFRP) method to IS research. The EFRP method allows one to synchronize eye tracking with electroencephalographic (EEG) recording to precisely capture users’ neural activity at the exact time at which they start to cognitively process a stimulus (e.g., event on the screen). This complements and overcomes some of the shortcomings of the traditional event related potential (ERP) method, which can only stamp the time at which a stimulus is presented to a user. Thus, we propose a method conjecture of the superiority of EFRP over ERP for capturing the cognitive processing of a stimulus when such cognitive processing is not necessarily synchronized with the time at which the stimulus appears. We illustrate the EFRP method with an experiment in a natural IS use context in which we asked users to read an industry report while email pop-up notifications arrived on their screen. The results support our proposed hypotheses and show three distinct neural processes associated with 1) the attentional reaction to email pop-up notification, 2) the cognitive processing of the email pop-up notification, and 3) the motor planning activity involved in opening or not the email. Furthermore, further analyses of the data gathered in the experiment serve to validate our method conjecture about the superiority of the EFRP method over the ERP in natural IS use contexts. In addition to the experiment, our study discusses important IS research questions that could be pursued with the aid of EFRP, and describes a set of guidelines to help IS researchers use this method.Social Sciences and Humanities Research Council of Canada (SSHRC)Natural Sciences and Engineering Research Council of CanadaFonds Québécois pour la Recherche sur la Société et la Culture (FQRSC)Fonds de recherche Nature et Technologies (FQRNT

Quantum dynamics in strong fluctuating fields

A large number of multifaceted quantum transport processes in molecular systems and physical nanosystems can be treated in terms of quantum relaxation processes which couple to one or several fluctuating environments. A thermal equilibrium environment can conveniently be modelled by a thermal bath of harmonic oscillators. An archetype situation provides a two-state dissipative quantum dynamics, commonly known under the label of a spin-boson dynamics. An interesting and nontrivial physical situation emerges, however, when the quantum dynamics evolves far away from thermal equilibrium. This occurs, for example, when a charge transferring medium possesses nonequilibrium degrees of freedom, or when a strong time-dependent control field is applied externally. Accordingly, certain parameters of underlying quantum subsystem acquire stochastic character. Herein, we review the general theoretical framework which is based on the method of projector operators, yielding the quantum master equations for systems that are exposed to strong external fields. This allows one to investigate on a common basis the influence of nonequilibrium fluctuations and periodic electrical fields on quantum transport processes. Most importantly, such strong fluctuating fields induce a whole variety of nonlinear and nonequilibrium phenomena. A characteristic feature of such dynamics is the absence of thermal (quantum) detailed balance.Comment: review article, Advances in Physics (2005), in pres

Explicit and Implicit Antecedents of Users' Behavioral Beliefs in Information Systems: A Neuropsychological Investigation

This is the author accepted manuscript. The final version is available from Taylor & Francis via the DOI in this recordBehavioral beliefs—perceived usefulness and perceived ease of use—have been identified as the most influential antecedents of individuals' information systems use intentions and behaviors within the technology acceptance model. However, little research has been aimed at investigating the implicit (automatic or unconscious) determinants of such cognitive beliefs, and more importantly, the potential nonlinear relationships of such antecedents with explicit (perceptual) ones. As such, this paper theorizes that implicit neurophysiological states—memory load and distraction— and explicit—engagement and frustration—antecedents interact in the formation of perceived usefulness and perceived ease of use. To test the study's hypotheses, we conducted an experiment that measured neurophysiological states while individuals worked on instrumental and hedonic tasks using technology. The results show that, as theorized, implicit and explicit constructs interact together, and thus have a nonlinear effect on behavioral beliefs. Specifically, when engagement is high, neurophysiological distraction does not statistically significantly affect perceived usefulness, whereas when engagement is low, neurophysiological distraction has a negative and significant effect on usefulness. The results also show that when frustration is high, neurophysiological memory load has a negative effect on perceived ease of use, whereas when it is low, neurophysiological memory load has a positive effect on perceived ease of use. This study makes several contributions to acceptance research and the emerging field of NeuroIS, including demonstration of the importance of emotional perceptions for moderating the effects of neurophysiological states on behavioral beliefs.Social Sciences and Humanities Research Council of Canada (SSHRC)Fonds Québécois pour la Recherche sur la Société et la Culture (FQRSC

Quantitative approaches to content analysis: identifying conceptual drift across publication outlets

Unstructured text data, such as emails, blogs, contracts, academic publications, organizational documents, transcribed interviews, and even tweets, are important sources of data in Information Systems research. Various forms of qualitative analysis of the content of these data exist and have revealed important insights. Yet, to date, these analyses have been hampered by limitations of human coding of large data sets, and by bias due to human interpretation. In this paper, we compare and combine two quantitative analysis techniques to demonstrate the capabilities of computational analysis for content analysis of unstructured text. Specifically, we seek to demonstrate how two quantitative analytic methods, viz., Latent Semantic Analysis and data mining, can aid researchers in revealing core content topic areas in large (or small) data sets, and in visualizing how these concepts evolve, migrate, converge or diverge over time. We exemplify the complementary application of these techniques through an examination of a 25-year sample of abstracts from selected journals in Information Systems, Management, and Accounting disciplines. Through this work, we explore the capabilities of two computational techniques, and show how these techniques can be used to gather insights from a large corpus of unstructured text