This research project investigated whether it was possible to reliably predict
adjudicators’ decisions made under the Housing Grants, Construction and
Regeneration Act 1996 (as amended). Anecdotally, many commentators had
suggested that such decisions were unpredictable and that led to significant
uncertainty in seeking to resolve construction disputes. If adjudicators’ decisions
could be reliably predicted it is foreseeable that the level of disputes referred to
Statutory Adjudication would reduce significantly, saving substantial sums in
unrecoverable costs that parties would otherwise incur. It is further foreseeable that
the construction industry would refocus resources onto projects and seek to deliver
on time, to budget and quality rather than diverting resources to deal with disputes.
The matter was investigated by distributing a Research Questionnaire to adjudicators
in order to identify factors that might influence adjudicators in their decision-making
and to seek their views as to why decisions might be unpredictable. By considering
the current level of knowledge, industry experience and the views of adjudicators, it
was possible to identify factors that might impact the predictability of adjudicators’
decisions. It was then possible to develop an Explanatory Model followed by a
Predictive Model to determine whether decisions could be reliably predicted.
This research found that adjudicators’ decisions, based on a sample of 125 previously
made decisions, could be reliably predicted. The Predictive Model determined that
whether a party would win or lose an adjudication was correctly predicted in 95% of
decisions. In terms of the percentage of recovery that a party would achieve, this was
correctly predicted in 83% of the decisions.
This research concluded that the evidence supported a high degree of predictability
in adjudicators’ decisions within the sample. This suggests a significant potential to
improve efficiency and reduce the number of disputes in the construction industry.
History
Institution
Anglia Ruskin University
File version
Accepted version
Language
eng
Thesis name
MPhil
Thesis type
Masters
Thesis submission date
2017-09-01
Legacy posted date
2018-11-07
Legacy creation date
2018-11-07
Legacy Faculty/School/Department
Theses from Anglia Ruskin University/Faculty of Science and Technology