Neurois: How To Conduct a Neuroimaging Study to Inform Information Systems Research

This tutorial discusses the potential of using functional brain imaging tools and methods to inform Information Systems (IS) research, termed NeuroIS. Economics, marketing, and psychology literature are already exploiting the potential of brain imaging to enrich their theories, methods, and data. IS researchers are still largely unaware of this potential, with the exception of some recent studies that started to show the potential of functional brain imaging to enrich IS theory development. This tutorial describes how IS researchers can use functional brain imaging tools to complement existing data sources. It overviews several functional brain imaging tools and proposes a set of opportunities that IS researchers can draw upon to inform IS theories. It also offers several examples of potentially fertile intersections of brain imaging and IS research and discusses step-by-step directions on how to conduct a NeuroIS study. This discussion is followed by several examples of NeuroIS studies in the context of trust, distrust, and uncertainty in e-commerce, technology adoption and use that spawns interesting new insights and implications. The tutorial concludes by discussing the potential of functional brain imaging tools, aiming to enhance the tools, and data in the IS researchers’ portfolio

Tutorial on the use of functional Magnetic Resonance Imaging (fMRI) in IS Research

This tutorial will discuss a set of guidelines for conducting a functional Magnetic Resonance Imaging (fMRI) study in IS research. Given the increased interest in using neuroimaging tools in the IS discipline, this tutorial aims at presenting the key steps needed to conduct an fMRI study. The tutorial will capture the four key steps needed to undertake an fMRI study: (1) formulating the research question, (2) designing the fMRI protocol, (3) analyzing fMRI data, and (4) interpreting fMRI results. These steps will be illustrated with several comparative studies between psychometric self-reported measures of various IS constructs with their corresponding brain activations when subjects responded to the psychometric measures of these constructs while their brain activity was captured in an fMRI scanner. This tutorial will also discuss the extent and meaning of the correlations between the psychometric measures and the corresponding brain activations, drawing comparisons among these correlations in the more affective versus the more cognitive areas of the brain. The relative predictive power of brain and self-reported data will also be discussed. Finally, detailed guidelines for designing high-quality fMRI studies and for capturing the full descriptive and predictive power of brain data will be derived

Where Does TAM Reside in the Brain? The Neural Mechanisms Underlying Technology Adoption

Toward materializing the recently identified potential of cognitive neuroscience for IS research (Dimoka, Pavlou and Davis 2007), this paper demonstrates how functional neuroimaging tools can enhance our understanding of IS theories. Specifically, this study aims to uncover the neural mechanisms that underlie technology adoption by identifying the brain areas activated when users interact with websites that differ on their level of usefulness and ease of use. Besides localizing the neural correlates of the TAM constructs, this study helps understand their nature and dimensionality, as well as uncover hidden processes associated with intentions to use a system. The study also identifies certain technological antecedents of the TAM constructs, and shows that the brain activations associated with perceived usefulness and perceived ease of use predict selfreported intentions to use a system. The paper concludes by discussing the study’s implications for underscoring the potential of functional neuroimaging for IS research and the TAM literature

14P. Application of Neuroimaging Methods in IS Research: An fMRI Study of Online Recommendation Agents

Recommendation agents are deployed to give online consumers advice on products. This study focuses on how two demographic interface characteristics of online recommendation agents – ethnicity and gender – influence the way consumers agree with the product recommendations offered by anthropomorphic (humanoid) recommendation agents. Because consumers may not always straightforwardly self-report their true perceptions about entities that differ from them in their ethnicity and gender, this study applies neuroimaging methods (fMRI) to understand how the design of online recommendation agents can include anthropomorphic interfaces with different ethnicity and gender to enhance the interaction between consumers and agents. Subjects who either fully matched or fully mismatched with the ethnicity and gender of recommendation agents were asked to indicate their agreement with the advice provided by the recommendation agents while their brain activities were observed in an fMRI scanner. The results show that there is only activation in brain areas of intense emotion (amygdala) and fear of loss (insular cortex) when subjects disagree with a recommendation agent that does not match their ethnicity and gender, while there is no activation for recommendation agents that match their ethnicity and gender. The fMRI results suggest that ethnicity and gender mismatch spawns strong emotional responses in the brain, particularly among women

Introduction to Human-Computer Interaction: Informing Design Utilizing Behavioral, Neurophysiological, and Design Science Methods Minitrack

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Incorporating Social Presence in the Design of the Anthropomorphic Interface of Recommendation Agents: Insights from an fMRI Study

Recommendation agents (RAs) are regularly used in online environments to give consumers advice on products. Since social components of human-like RAs (humanoid avatars) are important components in their adoption and use, this study focuses on how the design of the anthropomorphic interface of RAs in terms of social demographics, namely ethnicity and gender, can enhance the RA’s social presence to facilitate their adoption. Since social presence has been shown in the literature to predict the adoption and use of RAs, we examine whether match or mismatch in terms of the anthropomorphic RA’s ethnicity and gender can enhance the user’s social interaction with an RA. To overcome concerns of social desirability bias and political correctness when users assess the social presence of RAs that vary in their ethnicity and gender, we conducted a functional Magnetic Resonance Imaging (fMRI) study to complement a traditional behavioral experiment. Our goal was to explain prior behavioral findings that showed that ethnicity (as opposed to gender) match is associated with higher social presence, particularly among women. Specifically, brain activity was captured in an fMRI scanner while users who varied on their ethnicity and gender to either match or mismatch the ethnicity and gender of four RAs evaluated each of the RAs on their social presence. Besides contributing to the neuroscience literature by identifying the brain activations that relate to social presence, the fMRI results shed light on the nature of social presence and explain earlier behavioral findings by showing gender differences in the neural correlates of social presence in terms of ethnicity and gender match and mismatch. Implications on designing anthropomorphic interfaces to embody social demographics to enhance social presence are discussed

Understanding and Mitigating Product Uncertainty in Online Auction Marketplaces

The Internet interface poses a difficulty for buyers in evaluating products online, particularly physical experience and durable goods, such as used cars. This increases buyers' product uncertainty, defined as the buyer's perceived estimate of the variance in product quality based on subjective probabilities about the product's characteristics and whether the product will perform as expected. However, the literature has largely ignored product uncertainty and mostly focused on mitigating buyer's seller uncertainty. To address this void, this study aims to conceptualize the construct of product uncertainty and propose its antecedents and consequences in online auction marketplaces. First, drawing upon the theory of markets with asymmetric information, we propose product uncertainty to be distinct from, yet affected by, seller uncertainty. Second, based on auction pricing theory, we propose that product uncertainty and seller uncertainty negatively affect two key success outcomes of online marketplaces: price premium and transaction activity. Third, following information signaling theory, we propose a set of product information signals to mitigate product uncertainty: (1) online product descriptions (textual, visual, multimedia); (2) third-party product certifications (inspection, history report, warranty); (3) auction posted prices (reserve, starting, buy-it-now); and (4) intrinsic product characteristics (book value and usage). Finally, we propose that the effect of online product descriptions and intrinsic product characteristics on product uncertainty is moderated by seller uncertainty. The proposed model is supported by a unique dataset comprised of a combination of primary (survey) data drawn from 331 buyers who bid upon a used car on eBay Motors, matched with secondary transaction data from the corresponding online auctions. The results distinguish between product and seller uncertainty, show the stronger role of product uncertainty on price premiums and transaction activity compared to seller uncertainty, empirically identify the most influential product information signals, and support the mediating role of product uncertainty. This paper contributes to and has implications for better understanding the nature and role of product uncertainty, identifying mechanisms for mitigating product uncertainty, and demonstrating complementarities between product and seller information signals. The model's generalizability and implications are discussed