- Title
- Predicting production-output performance within a complex business environment: from singular to multi-dimensional observations in evaluation
- Creator
- Liu, Henry J.; Love, Peter E. D.; Ma, Le; Sing, Michael C. P.
- Relation
- ARC.DP160102882 http://purl.org/au-research/grants/arc/DP160102882
- Relation
- International Journal of Production Research Vol. 59, Issue 24, p. 7390-7399
- Publisher Link
- http://dx.doi.org/10.1080/00207543.2020.1841316
- Publisher
- Taylor & Francis
- Resource Type
- journal article
- Date
- 2021
- Description
- Performance evaluation (also measurement) provides a mechanism to ensure that organisations thrive in today’s complex and dynamic business environment. Performance evaluation is widely used for not only interpreting present performance but also for examining the future production output. An understanding of the ‘future’ is reliant on ‘prediction’, which should consider potential uncertainties as a result of the change of time or the variations between organisations, sectors or regions. However, the development of the epistemology of prediction has been overlooked within the context of performance evaluation. This paper develops and expands an existing paradigm of prediction by addressing the multi-dimensional observation for predicting an organisation’s production performance under the auspices of evaluation and specifically in the context of a construction organisation that delivers infrastructure projects, which are acknowledged as being dynamic, uncertain and complex. Three advanced econometric models are applied by using the production-related data provided by a construction organisation to test the developed paradigm. The managerial implications of the research are discussed to ensure their relevance to practice. The empirical study presented in this paper provides a significant contribution to improving the practice of performance evaluation, which is essential for construction organisations to ensure positive business outcomes can be achieved.
- Subject
- production output; prediction; performance evaluation; infrastructure projects; multi-dimensional observations; SDG 9; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1439323
- Identifier
- uon:40891
- Identifier
- ISSN:0020-7543
- Language
- eng
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