Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Lecturers' turnover intention and intention to remain with the organization: a dynamic cross-lagged panel model estimation using the PLSe2 method

Lecturers' turnover intention and intention to remain with the organization: a dynamic... Although numerous studies have been conducted to explore the impact of various factors on employees' turnover intention and intention to remain with the organization, the relationship between these two constructs remains largely unexplored. Considering the significance of these constructs, particularly in the context of the COVID-19 pandemic, the authors aimed to investigate their association within an academic environment using a dynamic modeling approach.Design/methodology/approachThis study follows a quantitative approach and utilizes a longitudinal survey design. The authors utilized a cross-lagged panel model (CLPM) and employed the parametric efficient partial least squares (PLSe2) methodology to estimate the dynamic model using data gathered from lecturers associated with both public and private universities in Malaysia. In order to offer methodological insights to applied higher education researchers, the authors also compared the results with maximum likelihood (ML) estimation.FindingsThe findings of the authors' study indicate a reciprocal relationship between turnover intention and intention to remain with the organization, with intention to remain with the organization being a stronger predictor. Moreover, situational factors were found to have a greater influence on eliciting turnover intention within academic settings. As anticipated, the use of the PLSe2 methodology resulted in higher R2 values compared to ML estimation, thereby reinforcing the effectiveness of PLS-based methods in explanatory-predictive modeling in applied studies.Practical implicationsThe authors' findings suggest prioritizing policies that enhance training and consultation sessions to foster positive attitudes among lecturers. Positive attitudes significantly impact judgment-driven behaviors like turnover intention and intention to remain with the organization. Additionally, improving working environments, which indirectly influence judgment-driven behaviors through factors like affective work events, affect and attitudes, should also be considered.Originality/valueThis study pioneers the examination of the causal relationship between turnover intention and intention to remain with the organization, their stability over time and the association of changes in these variables using a dynamic CLPM in higher education. It introduces the novel application of the cutting-edge PLSe2 methodology in estimating a CLPM, providing valuable insights for researchers in explanatory-predictive modeling. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Applied Research in Higher Education Emerald Publishing

Lecturers' turnover intention and intention to remain with the organization: a dynamic cross-lagged panel model estimation using the PLSe2 method

Download PDF
15 pages

Loading next page...
 
/lp/emerald-publishing/lecturers-turnover-intention-and-intention-to-remain-with-the-N2Q0i4VGmf

References (62)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
2050-7003
DOI
10.1108/jarhe-06-2023-0234
Publisher site
See Article on Publisher Site

Abstract

Although numerous studies have been conducted to explore the impact of various factors on employees' turnover intention and intention to remain with the organization, the relationship between these two constructs remains largely unexplored. Considering the significance of these constructs, particularly in the context of the COVID-19 pandemic, the authors aimed to investigate their association within an academic environment using a dynamic modeling approach.Design/methodology/approachThis study follows a quantitative approach and utilizes a longitudinal survey design. The authors utilized a cross-lagged panel model (CLPM) and employed the parametric efficient partial least squares (PLSe2) methodology to estimate the dynamic model using data gathered from lecturers associated with both public and private universities in Malaysia. In order to offer methodological insights to applied higher education researchers, the authors also compared the results with maximum likelihood (ML) estimation.FindingsThe findings of the authors' study indicate a reciprocal relationship between turnover intention and intention to remain with the organization, with intention to remain with the organization being a stronger predictor. Moreover, situational factors were found to have a greater influence on eliciting turnover intention within academic settings. As anticipated, the use of the PLSe2 methodology resulted in higher R2 values compared to ML estimation, thereby reinforcing the effectiveness of PLS-based methods in explanatory-predictive modeling in applied studies.Practical implicationsThe authors' findings suggest prioritizing policies that enhance training and consultation sessions to foster positive attitudes among lecturers. Positive attitudes significantly impact judgment-driven behaviors like turnover intention and intention to remain with the organization. Additionally, improving working environments, which indirectly influence judgment-driven behaviors through factors like affective work events, affect and attitudes, should also be considered.Originality/valueThis study pioneers the examination of the causal relationship between turnover intention and intention to remain with the organization, their stability over time and the association of changes in these variables using a dynamic CLPM in higher education. It introduces the novel application of the cutting-edge PLSe2 methodology in estimating a CLPM, providing valuable insights for researchers in explanatory-predictive modeling.

Journal

Journal of Applied Research in Higher Education – Emerald Publishing

Published: Nov 27, 2024

Keywords: Turnover intention; Intention to remain with the organization; COVID-19 lockdown; Cross-lagged panel model (CLPM); PLSe2

There are no references for this article.