[AISWorld] Contents of Volume 18, Issue 10 (October) Journal of the Association for Information Systems (JAIS)

JAIS JAIS at comm.virginia.edu
Tue Oct 31 23:45:51 EDT 2017


Contents of Volume 18, Issue 10 (October) 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

Affect Infusion and Detection through Faces in Computer-mediated Knowledge-sharing Decisions

Dennis D. Fehrenbacher, Monash University

Abstract

Faces are important in both human communication and computer-mediated communication. In this study, I analyze the influence of emotional expressions in faces on knowledge-sharing decisions in a computer-mediated environment. I suggest that faces can be used for affect infusion and affect detection, which increases the effectiveness of knowledge-management systems. Using the affect infusion model, I discuss why emotions can be expected to influence knowledge-sharing decisions. Using the two-step primitive emotional contagion framework, I found that emotional facial expression attached to a knowledge-sharing request influenced knowledge-sharing decisions. This influence was mediated by the decision maker’s emotional valence in the facial expression tracked by Face Reader technology and held for females but not males. I discuss implications for designers of emotionally intelligent information systems and research.

To obtain a copy of the entire article, click on the link below:
Available at: http://aisel.aisnet.org/jais/vol18/iss9/2 


Editorial

Digital Business Convergence and Emerging Contested Fields: A Conceptual Framework

Dongback Seo, ChungBuk National University

Abstract

Due to innovations in digital technologies, organizations that used to practice their business in discrete industries now confront radically transformed environments with new competitors from other industries. To understand this phenomenon of digital business convergence, I adopt the theory of strategic action fields from the literature on social movements and organizations. I use elements of this theory to analyze the actions of organizations in contested fields instead of taking a vertical, horizontal, or single-industry perspective. Specifically, I look at how organizations use different types of mobilizabilities (political, social, and technological) to influence the emergence and evolution of contested fields. This paper raises research questions about digital business convergence, suggests ways to investigate those questions, and provides a conceptual framework to study organizations’ strategies and behaviors from a strategic action field perspective.

To obtain a copy of the entire article, click on the link below:
Available at: http://aisel.aisnet.org/jais/vol18/iss9/3 

Editorial

Controlling for Lexical Closeness in Survey Research: A Demonstration on the Technology Acceptance Model

D Gefen, Drexel University
Kai R. Larsen, University of Colorado

Abstract

Word co-occurrences in text carry lexical information that can be harvested by data-mining tools such as latent semantic analysis (LSA). In this research perspective paper, we demonstrate the potency of using such embedded information by demonstrating that the technology acceptance model (TAM) can be reconstructed significantly by analyzing unrelated newspaper articles. We suggest that part of the reason for the phenomenal statistical validity of TAM across contexts may be related to the lexical closeness among the keywords in its measurement items. We do so not to critique TAM but to praise the quality of its methodology. Next, putting that LSA reconstruction of TAM into perspective, we show that empirical data can provide a significantly better fitting model than LSA data can. Combined, the results raise the possibility that a significant portion of variance in survey based research results from word cooccurrences in the language itself regardless of the theory or context of the study. Addressing this possibility, we suggest a method to statistically control for lexical closeness.

To obtain a copy of the entire article, click on the link below:
Available at: http://aisel.aisnet.org/jais/vol18/iss9/1 



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






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