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In search of the pseudo-transformational leader: A person-centered approach

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  • Tian, Amy Wei
  • Meyer, John P.
  • Ilic-Balas, Tatjana
  • Espinoza, Jose A.
  • Pepper, Susan

Abstract

Unlike truly transformational leaders who inspire and empower others to achieve a collective good, pseudo-transformational leaders (pseudo-TL) do so for their own gain.Christie et al. (2011) found preliminary support for a behavioral model proposing that pseudo-TL display the same inspirational motivation as authentic-transformational leaders (authentic-TL), but fail to display other typical transformational behaviors. In this study, we take a person-centered approach to replicate and extend Christie et al.’s (2011) research. We included leader self-interest as an attributed quality of pseudo-TL along with the transformational leadership facets. In two studies (an experimental simulation, N = 154; a survey study, N = 292), we found that while both displayed inspirational motivation, compared to authentic-TL, pseudo-TL were rated lower on intellectual stimulation, individualized consideration and idealized influence.They were perceived as more self-interested, less trustworthy, and evoked lower levels of trust. We identified five distinct profiles in Study 2, suggesting the possible need to expand the concept of pseudo-TL beyond that proposed by Christie et al. (2011).

Suggested Citation

  • Tian, Amy Wei & Meyer, John P. & Ilic-Balas, Tatjana & Espinoza, Jose A. & Pepper, Susan, 2023. "In search of the pseudo-transformational leader: A person-centered approach," Journal of Business Research, Elsevier, vol. 158(C).
    • Handle: RePEc:eee:jbrese:v:158:y:2023:i:c:s0148296323000334
    DOI: 10.1016/j.jbusres.2023.113675
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    References listed on IDEAS

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    1. Venkatram Ramaswamy & Wayne S. Desarbo & David J. Reibstein & William T. Robinson, 1993. "An Empirical Pooling Approach for Estimating Marketing Mix Elasticities with PIMS Data," Marketing Science, INFORMS, vol. 12(1), pages 103-124.
    2. Vermunt, Jeroen K., 2010. "Latent Class Modeling with Covariates: Two Improved Three-Step Approaches," Political Analysis, Cambridge University Press, vol. 18(4), pages 450-469.
    3. Julian Barling & Amy Christie & Nick Turner, 2008. "Pseudo-Transformational Leadership: Towards the Development and Test of a Model," Journal of Business Ethics, Springer, vol. 81(4), pages 851-861, September.
    4. Bengt Muthén & Kerby Shedden, 1999. "Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm," Biometrics, The International Biometric Society, vol. 55(2), pages 463-469, June.
    5. Syed, Fauzia & Naseer, Saima & Nawaz, Javaria & Shah, Syed Zulfiqar Ali, 2021. "When the victim becomes vicious: Combined effects of pseudo transformational leadership and epistemic motivation on contempt and deviant behaviors," European Management Journal, Elsevier, vol. 39(2), pages 236-246.
    6. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    7. Stanley Sclove, 1987. "Application of model-selection criteria to some problems in multivariate analysis," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 333-343, September.
    8. W. Gibson, 1959. "Three multivariate models: Factor analysis, latent structure analysis, and latent profile analysis," Psychometrika, Springer;The Psychometric Society, vol. 24(3), pages 229-252, September.
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