An investigation of ingratiation as a career management strategy: evidence from Singapore

In spite of the recognition that career politics, and therefore ingratiation, is widely used to manage careers, there is a dearth of empirical studies on ingratiation as a career management strategy. Consistent with Ferris and Kacmar's (1988) suggestion, the study reported here investigated the conditions (defined by situational variables, career concerns and personality variables) under which ingratiation is used as a career management strategy. Data were obtained through a structured questionnaire from professional employees (N = 214) in public and private sector organizations in Singapore.

Factor analysis of the 21-item career concerns scale revealed four solutions representing internal and external career concerns. The results of the hierarchical regression revealed that situational variables explained most of the variance in ingratiation, followed by career concerns and personality variables. Among the individual variables, task ambiguity, supervisor reward power, managerial responsibility and need for achievement were significant while personal success approached significance. Internal career concern of autonomy development was unrelated to ingratiation. Of the interaction terms, only supervisor reward power and Machiavellianism approached significance. Directions for future studies are suggested.     

A quasi-maximum likelihood method for estimating the parameters of multivariate diffusions

A quasi-maximum likelihood procedure for estimating the parameters of multi-dimensional diffusions is developed in which the transitional density is a multivariate Gaussian density with first and second moments approximating the true moments of the unknown density. For affine drift and diffusion functions, the moments are exactly those of the true transitional density and for nonlinear drift and diffusion functions the approximation is extremely good and is as effective as alternative methods based on likelihood approximations. The estimation procedure generalises to models with latent factors. A conditioning procedure is developed that allows parameter estimation in the absence of proxies.