Gabriel Cepeda, José L. Roldán, Misty A. Sabol, Joe Hair, Alain Yee Loong Chong
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This article summarizes the findings of an analysis of the adoption and reporting of partial least squares structural equation modeling (PLS-SEM) analytical tools by Industrial Management & Data Systems authors in the most recent five-year period.Design/methodology/approach Selected emerging advanced PLS-SEM analytical tools that have experienced limited adoption are highlighted to broaden awareness of their value to IS researchers.Findings PLS-SEM analytical tools that facilitate understanding increasingly complex theoretical models and deliver improved prediction assessment are now available. IS researchers should explore the opportunities to apply these new tools to more fully describe the contributions of their research.Research limitations/implications Findings demonstrate the increasing acceptance of PLS-SEM as a useful alternative research methodology within IS. PLS-SEM is a preferred structural equation modeling (SEM) method in many research settings and will become even more widely applied when IS researchers are aware of and apply the new analytical tools.Practical implications Emerging PLS-SEM methodological developments will help IS researchers examine new theoretical concepts and relationships and publish their work. Researchers are encouraged to engage in more complete analyses by applying the applicable emerging tools.Originality/value Applications of PLS-SEM for prediction, theory testing and confirmation have increased in recent years. Information system scholars should continue to exercise sound practice by applying these new analytical tools where applicable. Recommended guidelines following Hair et al. 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引用次数: 0
摘要
目的 在信息系统(IS)研究中严格应用分析工具对于开发新知识和领域创新非常重要。新兴工具为未来的探索、实践和创新提供了基石。本文总结了《工业管理与数据系统》作者在最近五年中对偏最小二乘法结构方程建模(PLS-SEM)分析工具的采用和报告的分析结果。IS 研究人员应探索应用这些新工具的机会,以更全面地描述其研究成果。研究局限性/影响 研究结果表明,PLS-SEM 作为 IS 领域的另一种有用研究方法,已被越来越多的人所接受。在许多研究环境中,PLS-SEM 是一种首选的结构方程建模(SEM)方法,当 IS 研究人员了解并应用新的分析工具时,PLS-SEM 将得到更广泛的应用。鼓励研究人员应用适用的新兴工具进行更全面的分析。信息系统学者应在适用的情况下应用这些新的分析工具,从而继续进行合理的实践。其中包括遵循 Hair 等人(2019;2022)的建议指南。
Emerging opportunities for information systems researchers to expand their PLS-SEM analytical toolbox
Purpose Rigorous applications of analytical tools in information systems (IS) research are important for developing new knowledge and innovations in the field. Emerging tools provide building blocks for future inquiry, practice and innovation. This article summarizes the findings of an analysis of the adoption and reporting of partial least squares structural equation modeling (PLS-SEM) analytical tools by Industrial Management & Data Systems authors in the most recent five-year period.Design/methodology/approach Selected emerging advanced PLS-SEM analytical tools that have experienced limited adoption are highlighted to broaden awareness of their value to IS researchers.Findings PLS-SEM analytical tools that facilitate understanding increasingly complex theoretical models and deliver improved prediction assessment are now available. IS researchers should explore the opportunities to apply these new tools to more fully describe the contributions of their research.Research limitations/implications Findings demonstrate the increasing acceptance of PLS-SEM as a useful alternative research methodology within IS. PLS-SEM is a preferred structural equation modeling (SEM) method in many research settings and will become even more widely applied when IS researchers are aware of and apply the new analytical tools.Practical implications Emerging PLS-SEM methodological developments will help IS researchers examine new theoretical concepts and relationships and publish their work. Researchers are encouraged to engage in more complete analyses by applying the applicable emerging tools.Originality/value Applications of PLS-SEM for prediction, theory testing and confirmation have increased in recent years. Information system scholars should continue to exercise sound practice by applying these new analytical tools where applicable. Recommended guidelines following Hair et al. (2019; 2022) are included.