Open Access Open Access  Restricted Access Subscription or Fee Access

The Mediation Effect of Personal Characteristics on Organisational Behaviour

Sathiya V., Senthamarai Kannan K.


The concept of job satisfaction has been developed in many ways by many different researchers and practitioners. The Data is collected from 19 Arts and Science Colleges in Tirunelveli District. Employee Satisfaction is developed with dependence on Employee Involvement, Workplace Conflict, Working Condition. Employee retention is developed with dependence on Employee Involvement, Workplace Conflict, Working Condition. The Recursive SEM is used to test the hypothesis and solve the model using ADF Estimators. The fitted model is Indirect Path Model. In this model, the variables Employee Involvement, Workplace Conflict, Working Condition are indirect effect on Employee retention. Here, Satisfaction is the Mediator between them. In this case, the direct effect of Employee Involvement, Workplace Conflict, Working Condition on Employee retention is Zero. This is known as Mediation Model. By using employee satisfaction as a mediation effect on the model, the model is better fit. In the research, the relationship between Workplace Conflict and Employee satisfaction have the significant result and can be supported This study presents direct and indirect effect on path model. Among Direct and Indirect Path Model, the Indirect Path Model is better than direct path model. The Indirect Path Model is known as the Mediation Model. This fitted model indirect path model is Recursive SEM Model.

Keywords: Employee satisfaction, Workplace Conflict, Direct Path Model, Indirect Path Model.

Cite this Article
Senthamarai Kannan K., Sathiya V. The Mediation Effect of Personal Characteristics on Organisational Behaviour. Research & Reviews: Journal of Statistics. 2019; 8(2): 34–40p.


Mediation Effect,Structural Equation Modeling,Employee Involvement,Indirect Path Model,ADF Estimators

Full Text:



Amemiya, T. (1985). Advanced econometrics. Cambridge, MA: Harvard University Press.

Andrews, D. W. K. (1991). Heteroscedasiticty and autocorrelation consistent covariance matrix estimation. Econometrica, 59, 817–858.

Angrist, J. D., & Pischke, J.-S. (2008). Mostly harmless econometrics. Princeton, NJ: Princeton University Press.

Blau, P. M., & Duncan, O. D. (1967). The American occupational structure. New York, NY: Wiley.

Bollen, K. A., & Bauer, D. J. (2004). Automating the selection of model implied instrumental variables. Sociological Methods & Research, 30, 425–452.

Byrne, B. M. (2012). Structural equation modeling with Mplus. New York, NY: Routledge.

Carlos Brito and Judea Pearl (2002) A New Identification Condition for Recursive Models With Correlated Errors Structural Equation Modeling, 9(4), 459-474

Finney, S. J., & DiStefano, C. (2013). Non-normal and categorical data in structural equation modeling. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: Asecond course (pp. 269–314). Greenwood, CT: Information Age.

Greene,W. H. (1997). Econometric analysis. Englewood Cliffs, NJ. Prentice Hall.

Holbert, R. Lance; Stephenson, Michael T..(2003) The Importance of Indirect Effects in Media Effects Research: Testing for Mediation in Structural Equation Modeling Journal of Broadcasting & Electronic Media 47(4):556-572

Holmes Finch.W. & Brian F. French (2015). Modeling of Non-recursive Structural Equation Models With Categorical Indicators, Structural Equation Modeling: A Multidisciplinary Journal, 22:3, 416-428.

J.D. Boardman, Stress and physical health: The role of neighborhoods as mediating and moderating mechanisms, Soc. Sci. Med. 58 (2004), pp. 2473–2483.

Joreskög, K. G. (1983). Factor analysis as an error-in-variables model. In H. Wainer & S. Messick (Eds.), Principles of modern psychological measurement (pp. 185–196). Hillsdale, NJ: Erlbaum.

Jose,P.E.(2013). Doing statistical mediation and moderation. New York: Guilford Press

Kaplan, D. (1988). The impact of specification error on the estimation, testing, and improvement of structural equation models. Multivariate Behavioral Research, 23, 69–86.10

Kline, R. B. (2011). Principles and practice of structural equation modeling. New York, NY: Guilford.

Mulaik, S. A. (2010). Foundations of factor analysis. Boca Raton, FL: CRC Press.

Muthén, B. (1993). Goodness of fit with categorical and other non-normal variables. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 205–243). Newbury Park, CA: Sage.


  • There are currently no refbacks.