[AISWorld] Contents of Volume 18, Issue 4 (April) Journal of the Association for Information Systems (JAIS)

[JAIS]

Contents of Volume 18, Issue 4 (April) Journal of the Association for Information Systems (JAIS)

Contents of Volume 18, Issue 4 (April) Journal of the Association for Information Systems (JAIS), Official Publication of the Association for Information Systems
Published: Monthly Electronically
ISSN: 1536-9323
Published by the Association for Information Systems, Atlanta, USA (http://aisel.aisnet.org/jais/)

Editor-in-Chief:  Professor Suprateek Sarker, University of Virginia, USA

Paper
Brownie: A Platform for Conducting NeuroIS Experiments

Anuja Hariharan, Karlsruhe Institute of Technology, Karlsruhe, Germany
Marc T.P. Adam, The University of Newcastle, Australia
Verena Dorner, Karlsruhe Institute of Technology
Ewa Lux, Karlsruhe Institute of Technology
Marius B. Mueller, Karlsruhe Institute of Technology
Jella Pfeiffer, Karlsruhe Institute of Technology
Christof Weinhardt, Karlsruhe Institute of Technology

Abstract
In the NeuroIS field, experimental software needs to simultaneously present experimental stimuli to participants while recording, analyzing, or displaying neurophysiological measures. For example, a researcher might record a user’s heart beat (neurophysiological measure) as the user interacts with an e-commerce website (stimulus) to track changes in user arousal or show a user’s changing arousal levels during an exciting game. In this paper, we identify requirements for a NeuroIS experimental platform that we call Brownie and present its architecture and functionality. We then evaluate Brownie via a literature review and a case study that demonstrates Brownie’s capability to meet the requirements in a complex research context. We also verify Brownie’s usability via a quantitative study with prospective experimenters who implemented a test experiment in Brownie and an alternative software. We summarize the salient features of Brownie as follows: 1) it integrates neurophysiological measurements, 2) it incorporates real-time processing of neurophysiological data, 3) it facilitates research on individual and group behavior in the lab, 4) it offers a large variety of options for presenting experimental stimuli, and 5) it is open source and easily extensible with open source libraries. In summary, we conclude that Brownie is innovative in its potential to reduce barriers for IS researchers by fostering replicability and research collaboration and to support NeuroIS and interdisciplinary research in cognate areas, such as management, economics, or human-computer interaction.

To obtain a copy of the entire article, click on the link below:
http://aisel.aisnet.org/jais/vol18/iss4/3


Paper
Representing Crowd Knowledge: Guidelines for Conceptual Modeling of User-generated Content

Roman Lukyanenko, University of Saskatchewan
Jeffrey Parsons, Memorial University of Newfoundland
Yolanda Wiersma, Memorial University of Newfoundland
Gisela Wachinger, DIALOGIK, Non Profit Institute for Communication and Cooperation Research, Germany
Benjamin Huber, University of Stuttgart
Robert Meldt, University of Stuttgart

Abstract
Organizations’ increasing reliance on externally produced information, such as online user-generated content (UGC) and crowdsourcing, challenges common assumptions about conceptual modeling in information systems (IS) development. We demonstrate UGC’s societal importance, analyze its distinguishing characteristics, identify specific conceptual modeling challenges in this setting, evaluate traditional and recently proposed approaches to modeling UGC, propose a set of conceptual modeling guidelines for developing IS that harness structured UGC, and demonstrate how to implement and evaluate the proposed guidelines using a case of development of a real crowdsourcing (citizen science) IS. We conclude by considering implications for conceptual modeling research and practice.

To obtain a copy of the entire article, click on the link below:
http://aisel.aisnet.org/jais/vol18/iss4/2


Paper

An Extensive Examination of Regression Models with a Binary Outcome Variable

Suneel Babu Chatla, National Tsing Hua University
Galit Shmueli, National Tsing Hua University

Abstract
Linear regression is among the most popular statistical models in social sciences research, and researchers in various disciplines use linear probability models (LPMs)—linear regression models applied to a binary outcome. Surprisingly, LPMs are rare in the IS literature, where researchers typically use logit and probit models for binary outcomes. Researchers have examined specific aspects of LPMs’ but not thoroughly evaluated their practical pros and cons for different research goals under different scenarios. We perform an extensive simulation study to evaluate the advantages and dangers of LPMs, especially with respect to big data, which is now common in IS research. We evaluate LPMs for three common uses of binary outcome models: inference and estimation, prediction and classification, and selection bias. We compare its performance to logit and probit under different sample sizes, error distributions, and more. We find that coefficient directions, statistical significance, and marginal effects yield results similar to logit and probit. In addition, LPM estimators are consistent for the true parameters up to a multiplicative scalar. This scalar, although rarely required, can be estimated assuming an appropriate error distribution. For classification and selection bias, LPMs are on par with logit and probit models in terms of class separation and ranking and is a viable alternative in selection models. LPMs are lacking when the predicted probabilities are of interest because predicted probabilities can exceed the unit interval. We illustrate some of these results by modeling price in online auctions using data from eBay.

To obtain a copy of the entire article, click on the link below:
http://aisel.aisnet.org/jais/vol18/iss4/1


Elizabeth White Baker, PhD
Production Managing Editor, Journal of the AIS
jais at comm.virginia.edu