[AISWorld] Contents of Volume 19, Issue 7 (July) Journal of the Association for Information Systems (JAIS)

[JAIS]

Contents of Volume 19, Issue 7 (July) 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

Reinterpreting the Kuhnian Paradigm in Information Systems

Nik R. Hassan, University of Minnesota - Duluth
John Mingers, University of Kent

Abstract

The goal of this paper is to raise the level of discourse surrounding paradigms by drawing out a number of observations on how paradigms are interpreted in the IS field, and to reclaim the transformative potential of the Kuhnian paradigm concept in encouraging novel, interesting and relevant research and theorizing. After positioning the contribution of the Kuhnian paradigm and its significance in the philosophy of science, we describe the negative impacts of a research community’s preoccupation with the epistemological sense of paradigm, which ignited within the organizational sciences decades of unnecessary “paradigm wars” and a misplaced focus on methodology. We show how this epistemological rendering of paradigm, which is adopted by the IS field, differs from the opinions of well-known critics of Kuhn and how this view obscures the Kuhnian paradigm’s potential for innovative research. To provide valuable insights into these issues, we introduce Masterman’s interpretation of Kuhn’s model, which Kuhn himself endorses, and unpack the paradigm concept into its metaphysical, sociological and artifactual components. Using Masterman’s interpretation to highlight the primary meaning of Kuhn’s paradigm concept as model problem-solution and exemplar, we describe how this multifaceted transformative view of paradigm benefits the IS field.

To obtain a copy of the entire article, click on the link below:
Available at: http://aisel.aisnet.org/jais/vol19/iss7/6 


Paper

Demystifying the Influential IS Legends of Positivism

Mikko Siponen, University of Jyväskylä
Aggeliki Tsohou, Ionian University

Abstract

Positivism has been used to establish a standard that Information Systems (IS) research must meet to be scientific. According to such positivistic beliefs in IS, scientific research should: 1) be generalizable, 2) focus on stable independent variables, 3) have certain ontological assumptions, and 4) use statistical or quantitative methods rather than qualitative methods. We argue that logical positivist philosophers required none of these. On the contrary, logical positivist philosophers regarded philosophizing in general and ontological considerations in particular as nonsense. Moreover, the positivists’ preferred empirical research method was not a survey, but rather a qualitative observation recorded by field notes. In addition, positivist philosophers required neither statistical nor nonstatistical generalizability. At least some positivist philosophers also acknowledged the study of singular cases as being scientific. Many research orientations (e.g., single-setting research, examination of change, qualitative research) that are deemed “unscientific” by positivism in IS seem to be “scientific” (in principle) according to logical positivism. In turn, generally speaking, what has been justified as scientific by positivism in IS (e.g., requirements of statistical or nonstatistical generalizability, surveys, independent variables, ontological views) were either not required by logical positivists or were regarded as nonsensical by logical positivists. Furthermore, given that positivism is sometimes associated (or confused) with logical empiricism in IS, we also briefly discuss logical empiricism. Finally, realizing that certain influential, taken-for-granted assumptions that underlie IS research are unwarranted could have ground-breaking implications for future IS research.

To obtain a copy of the entire article, click on the link below:
Available at: http://aisel.aisnet.org/jais/vol19/iss7/5 

Paper

Solving the Interpretational-Confounding and Interpretational-Ambiguity Problems of Formative Construct Modeling in Behavioral Research: Proposing a Two-Stage Fixed-Weight Redundancy Approach

Chao-Min Chiu, National Sun Yat-Sen University
Jack Shih-Chieh Hsu, National Sun Yat-Sen University
Paul Benjamin Lowry, City University of Hong Kong
Ting-Peng Liang, National Sun Yat-Sen University

Abstract

Recently, information systems research has devoted increasing attention to formative measurements. However, current approaches to modeling formative constructs have potential validity problems and thus limited applicability. Here, we highlight two major problems in formative measurement—interpretational confounding and interpretational ambiguity—and propose a novel resolution. Interpretational confounding occurs when using the traditional free-estimation approach, because the weights of different formative indicators vary as the dependent variable changes, resulting in the distortion of the measurement weights of the focal formative construct and thus jeopardizing the generalizability of empirical tests. Another way to alleviate the interpretational-confounding issue is to include the multiple indicators multiple causes (MIMIC) construct in the path model (i.e., MIMIC-path). Unfortunately, this method has led to the second major problem of interpretational ambiguity, the existence of more than one potential explanation of the formative model. More specifically, reflective indicators in the MIMIC model can be viewed as (1) indicators of the MIMIC construct, (2) dependent variables of the formative construct, or (3) indicators of a reflective construct affected by independent variables (formative indicators). To resolve these issues, we propose a two-stage fixed-weight redundancy model (FWRM) approach. We demonstrate the applicability of the FWRM approach with a set of survey data. We conducted a simulation study evaluating the FWRM approach by comparing it with the commonly used free-estimation and MIMIC-path methods. The results indicate that our FWRM approach can indeed improve the validity of formative construct modeling by mitigating confounding and ambiguity issues.

To obtain a copy of the entire article, click on the link below:
Available at: http://aisel.aisnet.org/jais/vol19/iss7/4 




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