Multigroup Analysis in Information Systems Research using PLS-PM

IF 2.8 4区 管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE
Michael Klesel, Florian Schuberth, Björn Niehaves, J. Henseler
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引用次数: 12

Abstract

Heterogeneity is a pertinent issue in Information Systems (IS) research because human behavior often differs across groups. In the partial least squares path modeling (PLS-PM) context, several approaches have been proposed to investigate potential group differences. Despite the availability of numerous approaches, literature that compares their efficacy is sparse. Consequently, IS researchers lack guidance on which approach is best suited to detect group differences. We address this issue by presenting the results of an extensive Monte Carlo simulation study that juxtaposes the various approaches' behavior under numerous conditions. In doing so, we first provide an overview on existing approaches proposed for multigroup analysis (MGA) in the PLS-PM context. Moreover, we derive important implications for applied research: Firstly, we show that the omnibus test of group differences (OTG) and approaches based on the comparison of confidence intervals are not recommendable for MGA. Secondly, we provide detailed information as to which approaches are suitable for comparing one specific path coefficient and which are recommended if the complete structural model is compared across groups. Finally, we show that approaches which are designed to compare a single parameter require an adjustment for multiple comparisons when used to compare more than two groups.
PLS-PM在信息系统研究中的多群体分析
异质性是信息系统(is)研究中的一个相关问题,因为人类的行为在不同的群体中往往是不同的。在偏最小二乘路径建模(PLS-PM)的背景下,已经提出了几种方法来研究潜在的群体差异。尽管有许多可用的方法,但比较其功效的文献很少。因此,IS研究人员缺乏关于哪种方法最适合检测群体差异的指导。我们通过展示广泛的蒙特卡罗模拟研究的结果来解决这个问题,该研究将各种方法在许多条件下的行为并列。在此过程中,我们首先概述了在PLS-PM环境中提出的多组分析(MGA)的现有方法。此外,我们还得出了应用研究的重要启示:首先,我们表明基于置信区间比较的群体差异综合检验(OTG)和方法不适合用于MGA。其次,我们提供了详细的信息,说明哪些方法适合比较一个特定的路径系数,如果跨组比较完整的结构模型,哪些方法是推荐的。最后,我们表明,设计用于比较单个参数的方法在用于比较两个以上组时需要对多个比较进行调整。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Data Base for Advances in Information Systems
Data Base for Advances in Information Systems INFORMATION SCIENCE & LIBRARY SCIENCE-
CiteScore
3.60
自引率
7.10%
发文量
18
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