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Same model, same data, but different outcomes: Evaluating the impact of method choices in structural equation modeling.

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Title: Same model, same data, but different outcomes: Evaluating the impact of method choices in structural equation modeling.
Authors: Sarstedt, Marko1,2 (AUTHOR) sarstedt@lmu.de, Adler, Susanne J.1 (AUTHOR), Ringle, Christian M.3,4 (AUTHOR), Cho, Gyeongcheol5 (AUTHOR), Diamantopoulos, Adamantios6 (AUTHOR), Hwang, Heungsun7 (AUTHOR), Liengaard, Benjamin D.8 (AUTHOR)
Source: Journal of Product Innovation Management. Nov2024, Vol. 41 Issue 6, p1100-1117. 18p.
Subjects: Structural equation modeling, Research personnel, Decision making, Organizational change, Reproducible research
Abstract: Scientific research demands robust findings, yet variability in results persists due to researchers' decisions in data analysis. Despite strict adherence to state‐of the‐art methodological norms, research results can vary when analyzing the same data. This article aims to explore this variability by examining the impact of researchers' analytical decisions when using different approaches to structural equation modeling (SEM), a widely used method in innovation management to estimate cause–effect relationships between constructs and their indicator variables. For this purpose, we invited SEM experts to estimate a model on absorptive capacity's impact on organizational innovation and performance using different SEM estimators. The results show considerable variability in effect sizes and significance levels, depending on the researchers' analytical choices. Our research underscores the necessity of transparent analytical decisions, urging researchers to acknowledge their results' uncertainty, to implement robustness checks, and to document the results from different analytical workflows. Based on our findings, we provide recommendations and guidelines on how to address results variability. Our findings, conclusions, and recommendations aim to enhance research validity and reproducibility in innovation management, providing actionable and valuable insights for improved future research practices that lead to solid practical recommendations. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Product Innovation Management is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
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  Data: Same model, same data, but different outcomes: Evaluating the impact of method choices in structural equation modeling.
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  Data: <searchLink fieldCode="AR" term="%22Sarstedt%2C+Marko%22">Sarstedt, Marko</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> sarstedt@lmu.de</i><br /><searchLink fieldCode="AR" term="%22Adler%2C+Susanne+J%2E%22">Adler, Susanne J.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ringle%2C+Christian+M%2E%22">Ringle, Christian M.</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cho%2C+Gyeongcheol%22">Cho, Gyeongcheol</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Diamantopoulos%2C+Adamantios%22">Diamantopoulos, Adamantios</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hwang%2C+Heungsun%22">Hwang, Heungsun</searchLink><relatesTo>7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liengaard%2C+Benjamin+D%2E%22">Liengaard, Benjamin D.</searchLink><relatesTo>8</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Product+Innovation+Management%22">Journal of Product Innovation Management</searchLink>. Nov2024, Vol. 41 Issue 6, p1100-1117. 18p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Structural+equation+modeling%22">Structural equation modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Research+personnel%22">Research personnel</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Organizational+change%22">Organizational change</searchLink><br /><searchLink fieldCode="DE" term="%22Reproducible+research%22">Reproducible research</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Scientific research demands robust findings, yet variability in results persists due to researchers' decisions in data analysis. Despite strict adherence to state‐of the‐art methodological norms, research results can vary when analyzing the same data. This article aims to explore this variability by examining the impact of researchers' analytical decisions when using different approaches to structural equation modeling (SEM), a widely used method in innovation management to estimate cause–effect relationships between constructs and their indicator variables. For this purpose, we invited SEM experts to estimate a model on absorptive capacity's impact on organizational innovation and performance using different SEM estimators. The results show considerable variability in effect sizes and significance levels, depending on the researchers' analytical choices. Our research underscores the necessity of transparent analytical decisions, urging researchers to acknowledge their results' uncertainty, to implement robustness checks, and to document the results from different analytical workflows. Based on our findings, we provide recommendations and guidelines on how to address results variability. Our findings, conclusions, and recommendations aim to enhance research validity and reproducibility in innovation management, providing actionable and valuable insights for improved future research practices that lead to solid practical recommendations. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Product Innovation Management is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1111/jpim.12738
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        Text: English
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        PageCount: 18
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      – SubjectFull: Structural equation modeling
        Type: general
      – SubjectFull: Research personnel
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      – SubjectFull: Decision making
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      – SubjectFull: Organizational change
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      – TitleFull: Same model, same data, but different outcomes: Evaluating the impact of method choices in structural equation modeling.
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              Text: Nov2024
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              Y: 2024
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