Research Projects Overview

Thomas MeservyBrigham Young University

Automated Deception Detection Using Non-verbal Behavior

(with Judee Burgoon and Jay F. Nunamaker, Jr)

• Creation of a deception detection system based on non-verbal behavioral cues (body language and voice)

• Data collected at the US/Mexico border• System achieves 20-25% higher accuracy

than humans• Foundation for much of the work being

conducted at Univ. Arizona - CMI

Overview

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Utilizationcoefficients

Associationcoefficients

Representationcoefficients

Trait/State Distal Indicator Cues Proximal Percepts Attribution

Inferentialutilization

Externalization Perceptualrepresentation

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Functional validity

Accuracy coefficient

Controlled Semi-Controlled Natural

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Brain Activity AnalysisPolygraph

Near Infrared Spectroscopy

Micro-momentary Expressions

Statement Validity Assessment

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- Thermal Scanning

- Linguistic Analysis- Vocal Analysis- Movement Analysis

Segment into meaningful units

(questions)

Extract low-level features

Compute higher-level features

Summarize features across meaningful unit

Classify using most

discriminatory cues

Audio Files

Input Processing Output

Deceptive

Truthful

Head

Right Hand

Left Hand

Right Hand to Head Distance

Left Hand to Head

Distance

Right Hand to Left Hand

Distance

Discriminant AnalysisLogistic Regression

Multi-layer PerceptronSupport Vector Machine

Decision TreeLinear Regression

Discriminant AnalysisLogistic Regression

Multi-layer PerceptronSupport Vector Machine

Decision TreeLinear Regression

Video Files

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Response Latency Interruption

Subject TurnInterviewer

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Silent Pause

Evaluation of Competing Candidate Solutions in Electronic Networks of Practice

(with Matt Jensen & Kelly Fadel; ISR)

• What information influences knowledge seekers to select solutions when searching online?

• Elaboration Likelihood Model• Used eye-tracker to capture elaboration• 60 experienced software developers

were participants in field study

Overview

Evaluation of Competing Candidate Solutions in Electronic Networks of Practice

(with Matt Jensen & Kelly Fadel; ISR)

• Peripheral cues matter; especially validation• Validation is most salient cue• Even under intense elaboration, peripheral cues don’t

loose their power• Only under intense elaboration does content quality matter

Result

Exploring Knowledge Filtering Processes in Electronic Networks of Practice

(with Matt Jensen & Kelly Fadel; JMIS)

    Directionality

  Alternative-based processing Attribute-based processing

Constancy

Constant

(same # of attributes evaluated

across all solutions)

Individuals look at all attributes of the first solution, then move to the second solution and look at all attributes until all solutions have been considered; e.g., Additive model

Individuals look at one attribute across all solutions, then look at the second attribute across all solutions; all attributes for all solutions are evaluated; e.g., Additive difference model

Variable

(different # of attributes evaluated

across all solutions)

Individuals look at all attributes for a

single solution; select first solution

that satisfies all attribute thresholds;

e.g., Conjunctive model

Individuals look at one attribute

across all solutions and eliminate

from consideration any solution that

doesn’t satisfy the threshold for that

attribute;

e.g., Elimination by aspects model

    Directionality

  Alternative-based processing Attribute-based processing

Constancy

Constant

(same # of attributes evaluated

across all solutions)

Individuals look at all attributes of the first solution, then move to the second solution and look at all attributes until all solutions have been considered; e.g., Additive model

Individuals look at one attribute across all solutions, then look at the second attribute across all solutions; all attributes for all solutions are evaluated; e.g., Additive difference model

Variable

(different # of attributes evaluated

across all solutions)

Individuals look at all attributes for a

single solution; select first solution

that satisfies all attribute thresholds;

e.g., Conjunctive model

Individuals look at one attribute

across all solutions and eliminate

from consideration any solution that

doesn’t satisfy the threshold for that

attribute;

e.g., Elimination by aspects model

• Are there differences in filtering patterns for different levels of performance in an information seeking task?

Overview

    Directionality

  Alternative-based processing Attribute-based processing

Constancy

Constant (same # of attributes evaluated across all solutions)

Individuals look at all attributes of the first solution, then move to the second solution and look at all attributes until all solutions have been considered; e.g., Additive model

Individuals look at one attribute across all solutions, then look at the second attribute across all solutions; all attributes for all solutions are evaluated; e.g., Additive difference model

Variable(different # of attributes evaluated across all solutions)

Individuals look at all attributes for a single solution; select first solution that satisfies all attribute thresholds; e.g., Conjunctive model

Individuals look at one attribute across all solutions and eliminate from consideration any solution that doesn’t satisfy the threshold for that attribute; e.g., Elimination by aspects model

Exploring Knowledge Filtering Processes in Electronic Networks of Practice

(with Matt Jensen & Kelly Fadel; JMIS)

• Accuracy higher when filtering patterns employ – More constant evaluation of attributes across alternatives– More attribute-based –vs alternative-based processing

• Increased attribute-based processing in later filtering stages

Results

fMRI: Evaluation of Online Solutions(with Kelly Fadel, Ray Meservy; targeted ISR)

• How do individuals evaluate and adopt knowledge encountered in ENPs

• What cognitive processing occurs for different types of information?

• Central –vs- Peripheral; System 1 –vs- System 2

• Impact of different types of peripheral cues

Large Group Collaboration(with Joel Helquist and Amit Deokar; targeted CAIS)

• Few frameworks exist for characterizing large group collaborative endeavors

• Mass collaboration, including Web 2.0, is increasingly popular

• Conceptual framework characterizing emerging technologies

Scientific Ideation – Large Group Collaboration with Academics

(with Amit Deokar, Joel Helquist, Aaron Sainsbury; targeted at JMIS)

• Research process is broken especially in the social sciences– Long wait times, single channel,

isolated research agendas• Less Impact than what we

could have

Overview

• Communities of practice• Social capital• Individual motivations• Use technology to increase the

flow of information throughout the research process

Theories/Contribution

Information Addiction(with Kelly Fadel, Ray Meservy; targeted ISR)

• Addiction of all types has adverse impact• Technology addiction, including internet

addiction, is a recognized disorder with very real consequences

• Does Information addiction exist?• Are the mental processes for information

addiction similar to addiction patterns for other disorders?

• Screen for self-reported information addicts

• Gather information including logins for social media and other information sites

• fMRI – Present personalized information streams; self-rating of reaction

• Comparison of patterns

Approach

Riskiness in Online Social Media(with Shane Banks and Colin Onita; targeted at JMIS)

• Why do people post risky information on social media sites?

• How do fear affect behavioral intentions • Used custom designed snowballing

technique to solicit participants from social media site

• 600+ respondents• Protection Motivation Theory

Overview

Understanding Information Systems Continuance for Information Oriented Mobile Applications

(with Leida Chen and Mark Gillenson; CAIS)

• Information quality link to perceived usefulness• Quality of the system (e.g., availability,

responsiveness, flexibility) and quality of the process (e.g., ability to localize and personalize the information) lead to greater realization of the expected benefits

• Hedonic value (e.g., users’ feeling of joy, elation, fun, or pleasure, or depression associated with IS use) impacts the intention to continue to use IOMAs

Results

• Aims to understand the antecedents of consumers’ continuance behaviors in the context of information oriented mobile applications

• Mobile apps + ubiquitous access to information is becoming a reality

• Builds on Bhattacherjee’s work on IS Continuance

Overview

• Survey of users of Blackberry app “Instafind”, an Information Oriented Mobile Application

• 147 participants• SEM using AMOS

Data Collection & Analysis

Unleashing Agile Development in a Large Organization: How IS Loses Control

(with Lakshman Mahadevan & Bill Kettinger; CAIS)

• More varied control mechanisms • Clan control dominant control

mechanism• More frequent control points than in

traditional ISD processes• Business function exerts more control in

Agile than in traditional ISD processes

Results

• Case study at FedEx• Investigates dominant

control mechanisms during Agile software development

• Initial pilot of Agile processes

Overview