PLS-SEM: Indeed a Silver Bullet

By Joseph F. Hair, Christian M. Ringle, Marko Sarstedt. 2011

19,948 citations recorded by the source corpus.

Subfields: Information Systems and Management, Marketing, Organizational Behavior and Human Resource Management

View Paper: https://www.semanticscholar.org/paper/eae2faa5b4c771867fd54ea6436762028d56bcb6

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Citations in the corpus

Marketing Mix Modeling Using PLS-SEM, Bootstrapping the Model Coefficients (2021, background): “To find the empirical sampling distribution of a parameter, bootstrapping generates a number of samples with repetition (recommended: 5000) [4], containing the same amount of data as the original series to be sure that the samples obtained have the same statistical properties as the origin”
Environmental Knowledge, Awareness, and Business School Students’ Intentions to Purchase Green Vehicles in Emerging Countries (2018, neutral): “7, they can be considered reliable [62].”
The Impact of Self-Congruity and Evaluation of the Place on WOM: Perspectives of Tourism Destination Residents (2021, methodology): “Parametric test and Welch-Satterthwait test based on PLS-SEM were run to illustrate any significant differences in the relationship testing between groups (Sarstedt, Henseler, and Ringle 2011).”
Financing buyer–supplier dyads: an empirical analysis on financial collaboration in the supply chain (2016, background): “Several reasons, for example, the presence of formative constructs, the exploratory nature of the study and the medium sample size, justify this choice (Hair, Ringle, and Sarstedt 2011; Henseler et al. 2014).”
How Do Leaders Influence Innovation and Creativity in Employees? The Mediating Role of Intrinsic Motivation (2021, background): “For the internal consistency, the coefficients of composite reliability (CR) vary from 0.836 to 0.932 which satisfy the minimum acceptable levels (Hair et al., 2011).”
Business model innovation and firm performance: Exploring causal mechanisms in SMEs (2021, methodology): “We used PLS-SEM due to threefold advantages, (i) it allows to incorporate both reflective and formative measures, (ii) it is suitable for large and complex models and (iii) it has less restrictive assumptions about M.-A. Latifi et al. Technovation 107 (2021) 102274 the data (Hair et al., 2011).”