A HIGH-ORDER PERFORMANCE FRAMEWORK: CONTENT, STRUCTURE AND PERSONALITY ANTECEDENTS

Individual work performance is a central construct in industrial and organisational psychology, yet insufficient is known about it and little agreement has been achieved as to the content and structure of the performance domain. This thesis contributes to a better understanding of work performance, firstly through the establishment of a unified high-order performance taxonomy that has good content and construct validity, and second, through the identification of its personality antecedents by linking this performance taxonomy to a personality taxonomy. Additionally, this thesis draws on recent re-conceptualisations of personality, such that implications from state-level personality, in addition to trait-level personality, are considered.
In Study 1, I use an inductively developed performance taxonomy, the Great Eight framework, as a means of revealing a high-order performance structure. By using a cross-sectional survey design, performance rating data were collected from employees (N=242) and supervisors (N=158) within a Chinese organisation. In Study 2, using data collected from the same sample in two waves, I link this high-order performance structure to the Big Five personality taxonomy, so as to explore the potential for building one-to-one mapping between the two frameworks. In Study 3, I validate in a separate MBA student sample (N=98) the mapping between personality taxonomy and performance taxonomy, as identified in Study 2; additionally, I use a diary study design to measure state-level personality and to investigate whether personality – performance linkages can be further strengthened.
The findings from this thesis reveal that a four-factor model can best represent the high-order structure of the performance domain, and there is initial support for linking this taxonomy to the Big Five personality taxonomy. The results also indicate that state-level personality, especially within-person variability across time, has meaningful value in predicting performance outcomes.