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The development of a culture-based tool to predict team performance

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posted on 2014-11-20, 09:55 authored by Allan Hodgson
The effect of national culture on the performance of teams is becoming an increasingly important issue in advanced western countries. There are many interlinked reasons for this, including the increasing globalisation of companies and the use of joint ventures for the development of expensive platforms. A further issue relates to the export of complex sociotechnical systems, where a culture clash between designer/manufacturer and user can lead to significant problems. This report describes research work that was carried out to analyse the cultural factors that influence the performance of teams (including researchers, designers, operators and crews), and to determine whether these factors could be captured in a tool to provide assistance to team managers and team builders. The original point of interest related to the development of increasingly complex sociotechnical systems, for example nuclear power stations, oil refineries, offshore oil platforms, hospital systems and large transport aircraft. Answers that might be sought, in particular by the senior managers of global companies, include (1) the best teams (or best national locations) for fundamental research, industrial research & development, product/system improvement and other key activities, and (2) the implications for system performance and, as a result, for system design, of targeting an eastern Asian market, a South-American market, etc. A literature review was carried out of the effects of culture on team performance, of culture measures and tools and of task classifications; in addition, empirical evidence of the validity of measures and tools was sought. Significant evidence was found of the effects of culture on teams and crews, but no national culture-based team performance prediction tools were found. Based on the results of the literature review, Hofstede's original four-dimension cultural framework was selected as the basis for the collection and analysis of empirical data, including the results of studies from the literature and the researcher s own empirical studies. No team or task classification system was found that was suitable for the purposes of linking culture to team performance, so a five-factor task classification was developed, based on the literature review, to form the basis of the initial modelling work. A detailed analysis of results from the literature and from the author s pilot studies revealed additional culture-performance relationships, including those relating to cultural diversity. Three culture-performance models were incorporated into software tools that offered performance prediction capabilities. The first model was primarily a test bed for ideas; the second model incorporated a task/behavioural approach which achieved limited success; the third and final model was evaluated against a range of team and crew performance data before being tested successfully for acceptability by users. The research results included the discovery that the effects of cultural diversity must be sought at the individual cultural dimension level not at the composite level, that the effects of national culture on team performance are consistent and strong enough to be usefully captured in a predictive culture tool and that the relation¬ship between culture and behaviour is moderated by contextual factors.

Funding

EPSRC

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Publisher

© Allan Hodgson

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2014

Notes

A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.

EThOS Persistent ID

uk.bl.ethos.631655

Language

  • en

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    Mechanical, Electrical and Manufacturing Engineering Theses

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