Modelling and predicting the performance of cross border managers

Abstract

Purpose

Cross border managers are an important feature of the global economy. Despite this, research evidence suggests that the primary selection criteria for cross border managers are technical expertise and domestic business knowledge. This has resulted in insufficient numbers of high calibre candidates to meet the demands of today's global business context. This paper aims to argue that an understanding of an individual's value orientations is important for selecting cross border managers and predicting subsequent performance.

Design/methodology/approach

The paper reports the testing of a multidimensional value orientated taxonomy on a sample of 658 managers employed by three multinational organisations. The model was tested via SEM. OLS multiple regression was carried out to identify whether the dimensions of the taxonomy predict the performance of managers in cross border roles.

Findings

Results yielded sound factor structure of the taxonomy with a single factor solution identified on each of the two individual value dimensions. SEM confirmed significant relationships and a sound goodness‐of‐fit of the model. OLS regression results indicated that the model accurately predicted the performance of managers during cross border assignments.

Research limitations/implications

Surveys were administered at one point in time and do not account for any change in value orientations.

Practical implications

The model and results provide guidance to HRM professionals for selecting candidates for cross border business roles.

Originality/value

The study addresses a limitation of earlier work by testing the efficacy of the multi‐dimensional taxonomy with a larger and more diverse sample. The paper evaluates the strength of the taxonomy in predicting performance.

Citation

Cogin, J. and Fish, A. (2010), "Modelling and predicting the performance of cross border managers", Personnel Review, Vol. 39 No. 4, pp. 432-447. https://doi.org/10.1108/00483481011045407