PLS-SEM for software engineering research: An introduction and survey

Daniel Russo, Klaas Jan Stol

Research output: Contribution to journalReview articlepeer-review

68 Citations (Scopus)
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Abstract

Software Engineering (SE) researchers are increasingly paying attention to organizational and human factors. Rather than focusing only on variables that can be directly measured, such as lines of code, SE research studies now also consider unobservable variables, such as organizational culture and trust. To measure such latent variables, SE scholars have adopted Partial Least Squares Structural Equation Modeling (PLS-SEM), which is one member of the larger SEM family of statistical analysis techniques. As the SE field is facing the introduction of new methods such as PLS-SEM, a key issue is that not much is known about how to evaluate such studies. To help SE researchers learn about PLS-SEM, we draw on the latest methodological literature on PLS-SEM to synthesize an introduction. Further, we conducted a survey of PLS-SEM studies in the SE literature and evaluated those based on recommended guidelines.

Original languageEnglish
Article number78
JournalACM Computing Surveys
Volume54
Issue number4
Number of pages38
ISSN0360-0300
DOIs
Publication statusPublished - Jul 2021

Bibliographical note

Funding Information:
This work was supported by Science Foundation Ireland grant 13/RC/2094_P2 and 15/SIRG/3293. Authors’ addresses: D. Russo, Department of Computer Science, Aalborg University, Selma Lagerlöfs Vej 300, 9220, Aalborg, Denmark; email: daniel.russo@cs.aau.dk; K.-J. Stol, Lero—The Irish Software Research Centre and University College Cork, School of Computer Science and Information Technology, Cork, Ireland; email: k.stol@ucc.ie.

Publisher Copyright:
© 2021 Owner/Author.

Keywords

  • Critical review
  • Partial least squares
  • Research methodology
  • Structural equation modeling

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