Prediction of performance outcomes for procurement of public-sector construction consultants for property management (Most outstanding paper in the 2018 Emerald Literati Awards; RICS Funded Research Project)

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Purpose – Public-sector construction clients in the UK and Australia have a clear objective to maximise potential and value for construction and infrastructure projects. Outcome-based performance predictive models, which link influencing factors to individual performance outcomes, were developed for the
public-sector property management clients. The paper aims to discuss this issue.

Design/methodology/approach – Combined qualitative-quantitative methods were used to examine the causal relationships between performance outcomes and input economic and job performance factors. Hypotheses on individual relationships generated by a literature review were refined using the findings from a qualitative multiple-case study of three universities, and then tested by a quantitative hierarchical regression analysis using data from 60 consultancies collected from a questionnaire survey sent to the estate management
offices of the universities, which form a unique public sector. Each performance project outcome was regressed against influencing factors. Performance predictive models were established in the form of regression equations.

Findings – Five performance outcomes are identified: time, cost, quality, innovations and working relationship with the client. These can be significantly predicted by regression models, based on performance influencing factors of project staff, competence of firm, execution approach, size of firm, consultant
framework and competition level.

Research limitations/implications – The performance predictive models developed should be regarded as “conceptual”. Public-sector clients may have different organisation objectives and hence different requirements for performance outcomes, which may further vary according to specific project situations.
The models should be adapted to suit individual needs. Adjustments can be made by using the combined qualitative-quantitative methods adopted in this research, thus creating customised models for property management and construction-related clients.

Practical implications – The client’s professional team should focus on the significant performance influencing factors and take advantage of the performance predictive models to select quality consultants. Construction consultants should address the factors in the tender proposals in order to add value to the project and benefit the client.

Originality/value – The existing input-based assessment approach applied at the tender stage cannot guarantee the strategic project objectives to be achieved. The performance predictive models are adaptable for property management and construction disciplines within the wider public sector, thus contributing to
achievement of the government construction policy.
Original languageEnglish
Pages (from-to) 433-447
Number of pages15
JournalProperty Management
Issue number4
Publication statusPublished - 2017

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