From General to Specific: Understanding Individual Characteristics and their Relationship with Neural Recordings during Media Consumption

From General to Specific: Understanding Individual Characteristics and their Relationship with Neural Recordings during Media Consumption

Adriane B. Randolph (Kennesaw State University, Kennesaw, GA, USA) and Janée N. Burkhalter (Saint Joseph's University, Philadelphia, PA, USA)
Copyright: © 2016 |Pages: 18
DOI: 10.4018/IJBIR.2016070103
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Abstract

Neurophysiological methods and recording techniques are increasingly being embraced to enhance business intelligence about consumers' behavior. Researchers have found evidence linking individual characteristics with variations in mental processing and user literacy for neurally-controlling computer interfaces. The work presented here seeks to better understand the relationship between individual characteristics and neural activations as recorded by electroencephalography (EEG) while participants viewed certain types of media online. A study conducted with 21 right-handed individuals demonstrates that the individual characteristics of smoking, hand dexterity, and experience playing certain types of video games correlate with neural activations in the frontal lobe, reflecting arousal and engagement. These correlations indicate the need to control for particular participant characteristics when conducting studies using neurophysiological recording techniques and expand considerations for incorporating such novel, yet insightful tools into the business intelligence practice.
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Introduction

Various fields of business are increasingly embracing neurophysiological methods and recording techniques to better understand consumers, end-users, and their behaviors. Such information is a cornerstone for driving decisions by managers in the form of business intelligence and analytics (Davenport, 2010; Wixom & Watson, 2010). Within the information systems (IS) field, researchers are using such techniques to validate theories (Dimoka et al., 2012; Dimoka, Pavlou, & Davis, 2011), to better understand trust (Dimoka, 2010), technology acceptance (Dimoka & Davis, 2008), and enhance human-computer interaction (HCI) (Riedl, Randolph, vom Brocke, Léger, & Dimoka, 2010). Outside of IS, researchers in other fields of business, such as marketing and economics, are also utilizing neural recording techniques to better understand behavior and decision-making (Camerer, 2005; Lee, Broderick, & Chamberlain, 2007; Sanfey, Loewenstein, McClure, & Cohen, 2006). These insights into consumer behavior may support managers’ decision-making as they are gathered, stored, and analyzed (Bose, 2011). However, such insights based on neurophysiological data may not yet be incorporated into “[business intelligence] for the masses” (Negash, 2004, p. 182) as is more likely with other common forms of data such as that collected using psychometric tools or gleaned from spreadsheet files.

With the increase in using neurophysiological techniques, researchers have found evidence linking individual characteristics with variations in mental processing about trust (Riedl, Hubert, & Kenning, 2010), behavior as a result of felt emotions (Leger, 2014), and user literacy for control of computer interfaces using neural input where some individuals have greater innate controllability than others (Allison & Neuper, 2010; Randolph, 2007, 2012). Characteristics are considered to be a person’s demographic and physiological traits, as well as his/her experience. These characteristics can vary across many dimensions. Differences based on characteristics such as gender, age, dexterity, and even hair color correlate with various recordings of neural activity (Randolph, Moore Jackson, & Karmakar, 2011; Randolph & Moore Jackson, 2010). With such findings, similar considerations should also hold for individual characteristics and their relationship to neural activations when assessing human mental states. Although neurophysiological recording techniques are built upon generalizations of the human brain, significant individual differences in brain patterns also exist, such as by gender, and have been found relevant to research in a variety of decision-making sciences (Ariely & Berns, 2010).

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