THE EFFECT OF FEEDBACK INFORMATION ON CONSTRUCTION CONTRACT BIDDING


The effect of information feedback in 
construction bidding 
 
Alexander Soo and Bee Lan Oo (School of Civil Engineering, University of Sydney) 
 
 

 
 
 
Abstract 
With the goal to achieve efficiency in bidding competitions, many codes of bidding procedure 
recommend clients provide contractors with bidding feedback information. Contractors strive 
to bid competitively via learning based on their experiences in past bidding attempts. The 
level of bidding feedback information, however, varies across clients. In many cases, clients 
do not provide feedback or provide insufficient feedback to contractors. Focussing on two 
information feedback conditions (full and partial), we examine: (i) the changes in bidding 
trend over time, and (ii) the effects of bidding feedback information on bidders’ 
competitiveness in bidding. Data were gathered using a bidding experiment that involved 
student (inexperienced) bidders with a construction project management background. The 
results show that the variations in bids over time for full information feedback condition are 
statistically significant, but not for bids from bidders with partial bidding feedback information. 
Bidders with full bidding feedback information are more competitive than those with partial 
bidding feedback information. The findings add to both our theoretical and empirical 
understanding of construction bidding: an understanding of the process of changes in the 
price of building work, and how the process can be manipulated through the release of 
bidding feedback information. 
 
Keywords: Bidding, Construction, Experiment, Feedback.  
 
 
Introduction 
Construction clients’ objective of awarding a contract is to agree upon a contract type and 
price that produces reasonable risk and maximises the incentive for efficient and economic 
performance of a contractor (Kerzner 2006). They will naturally aim to strike the best bargain 
by introducing some kind of competition, by some form of negotiation or by a mixture of the 
two. Descending first-price sealed-bid auctions are most commonly used in the industry 
where competing contractors submit independent bids (i.e., offers to sell construction 
services) and the lowest bidder wins at the lowest bid price. The competitive bidding is the 
driving force for contractors providing lower bid prices to suit the clients' construction and 
financial needs (Drew and Skitmore 1992). Nonetheless, Runeson (2000) noted that a bid 
offer must be equal to or above the minimum price at which a contractor is prepared to 
undertake also considering acceptable probability of profit without an unacceptable risk of 
loss.  
 
Contractors adopt various strategies to enhance their chances of winning projects. Their 
experiences in past bidding competitions play a role in offering competitive bid prices. Using 
bidding data of building projects, Fu et al. (2004) found that experienced bidders who bid 
frequently are more competitive than bidders who bid occasionally, where experiential 
learning in recurrent bidding plays a key role. In this, bidding feedback information is 
necessary to facilitate contractors’ learning (Kagel and Levin 2002). Varying information 
feedback conditions have been shown to affect a bidder's competitiveness to different 
degrees in sealed-bid auctions, whereby affecting the revenues for those accepting bids to 



 
Australasian Journal of Construction Economics and Building 

Soo,  A  and  Oo,  B  L  (2010)  ‘The  effect  of  information  feedback  in  construction  bidding’, Australasian  Journal  of 
Construction Economics and Building, 10 (1/2) 65‐75  

66 

buy or accepting offers to sell (e.g. Issac and Walker 1985; Dufwenberg and Gneezy 2002; 
Engelbrecht-Wiggans and Katok 2008).  
Many codes of bidding procedure recommend clients provide contractors with bidding 
feedback information (e.g. New South Wales Government 2005; Ministry of Finance 2005). 
The level of bidding feedback information, however, varies across clients. For example, 
while the Singapore Government releases the full list of bidders and their bids on the GeBIZ 
government website (www.gebiz.gov.sg), the New South Wales Government in Australia 
releases the details of successful bid only (www.tenders.nsw.gov.au). In many cases, clients 
do not provide feedback or provide insufficient feedback to contractors (Drew and Fellows 
1996). Although their survey respondents indicated that they obtain bidding data from a 
variety of sources, including: competitors, subcontractors, friendly acquaintances, suppliers 
and newspapers, little is known about the effects of varying levels of bidding feedback 
information on construction bid prices. This study aims to examine the effects of two 
information feedback conditions in construction bidding. Using an experimental approach, 
two groups of inexperienced bidders were randomly allocated to full and partial information 
feedback conditions, respectively. With regard to the two information feedback conditions, 
the specific objectives are to examine: (i) the changes in bidding trend over time, and (ii) the 
effects of bidding feedback information on bidders’ competitiveness in bidding. The study 
provides an insight into changes in the price of building work associated with the release of 
bidding feedback information. 
 
Effects of Information in Construction Bidding  
Information related to construction bidding can be separated into two categories, namely 
public information and feedback information. Public information, as the name implies, refers 
to information that is publicly available such as project type and size, project location and 
client identity. Feedback information refers to the information provided at the end of bidding 
competitions. Depending on clients’ procurement procedure, the levels of bidding feedback 
information given to contractors varies. Drew and Fellows (1996) identified that contractors 
use bidding feedback information for four different purposes: (i) for deciding on whether or 
not to bid for future projects, (ii) for determining mark-up for future projects, (iii) for analysing 
their bidding performance; and (iv) for analysing bidding performance of their competitors. 
 
Public Information 
There have been many studies on the effects of public information in competitive bidding. 
The leading article referenced by many authors appears to be that written by Milgrom and 
Weber (1982). They define public information being either a cost estimate provided by the 
client or project information such as geological data or proprietary information (private 
surveys, etc). In ascending first-price sealed-bid auctions (i.e., highest bidder wins at the 
highest bid price), they found that bidders bid more aggressively when public information is 
released, hence raising the seller's profit. In another study by de Silva et al. (2008), which 
examined the impact of public information on highway procurement where lowest bidder 
wins the job, they found that the release of a cost estimate on bridge procurement projects 
led to a sharp decrease in the bids received. The adjustment in bid prices from what is 
believed to be a more accurate cost estimate from the procurement agency suggests that 
bidders have a strong intent on winning the bid, and lowering the bid increases their chances 
of winning. These studies suggest greater revenues for those agents accepting bids or 
accepting offers. However, a recent study by de Silva et al. (2009) detected that bidders 
typically bid higher on procurement contracts from the Oklahoma Department of 
Transportation, following the release of cost estimate from the procurement agency which 
allowed them to readjust their bids in order to gain a higher profit margin.  
 
Feedback Information 
The effects of varying information feedback conditions on bidders’ bidding behaviour have 
been identified via experimental setting in conventional economics literature. These 



 
Australasian Journal of Construction Economics and Building 

Soo,  A  and  Oo,  B  L  (2010)  ‘The  effect  of  information  feedback  in  construction  bidding’, Australasian  Journal  of 
Construction Economics and Building, 10 (1/2) 65‐75  

67 

experiments were designed in two main settings, namely ascending first-price sealed-bid 
auction and descending first-price sealed-bid auction. In ascending first-price sealed-bid 
auctions, Engelbrecht-Wiggans and Katok (2008) conducted an experiment that involved 
student subjects to bid for a fictitious asset in 100 auctions. They found that revealing the 
winning bids led to a higher average bid, and that the critical feedback information is the 
winning bids, as other information feedback condition (full information about past bids) 
appears to exhibit variability (either an increasing or decreasing trend) in the student 
subjects’ bids. Similarly, Neugebauer and Perote (2008) detected that the release of winning 
bid triggers bidders in their experiment to base their bids on the winning bid, as opposed to a 
no information feedback condition where they must rely on their experiences through 
recurrent bidding. They found that the release of feedback information led to higher overall 
bids. Neugebauer and Selten (2006) have also detected overbidding in partial information 
feedback condition that lead to generally higher bids among subjects in their experiment that 
aimed to examine the effects of partial and no information feedback conditions on bidders’ 
behaviour. Another experiment by Issac and Walker (1985), however, shows that full 
information feedback condition produces lower bid prices in comparing the effects of full (all 
past bid) and partial (winning bid) information feedback conditions among naïve bidders in 
an ascending sealed-bid auction. In another study by Ockenfels and Selten (2005), they 
found that a no information feedback condition generally leads to higher bids in an 
ascending sealed-bid auction. Their research involved observing the upward and downward 
impulses of bidding subjects. The weighted impulse is defined in their research as a mix of 
winning utility and risk aversion strategies. They noted that bidding subjects with no 
feedback information were unable to consider the relative sizes of the downward and upward 
impulses and systematically underestimate the downward impulses leading to higher bids. 
  
In a descending first-price sealed-bid auction, Dufwenberg and Gneezy (2002) studied the 
effects of various information feedback conditions on bidders’ behaviour (i.e., revealing all 
bids, all winning bids or no bids) for ten rounds. They found that the condition where full 
bidding feedback information was provided led to much higher bid prices over the limited and 
no information feedback conditions, thereby raising the seller’s profit. Their results also show 
that information released about a bidder's own performance generally leads to a lower bid, 
which is in line with Engelbrecht-Wiggans and Katok’s (2008) finding that the winning price is 
the most critical feedback information. Moving towards a modelling attempt on feedback 
information in descending first-price auctions, Esponda (2008) suggests that the release of 
feedback information leads to an incorrect evaluation by bidders due to overestimation of the 
expected profit from the past bids. His empirical analysis shows that the feedback 
information does have a correlation to bid prices. 
 
Although the effects of different information feedback conditions on bidders’ behaviour seem 
to be mixed in the above experimental studies, it is clear that the experiment subjects had 
used the feedback information, to varying degree, for analysing the bidding performance of 
competing bidders. This would allow for insight into competitors’ bidding trends or 
movements (Friedman 1956). The information gained could be used to estimate the 
probable range of competitors' bids (Milgrom 1989), and to differentiate the less serious 
competitors from the more serious (McCaffer 1976). Kortanek et al. (1973) noted that a 
bidder’s bidding strategy which reflects its bidding behaviour at any time is a direct product 
of learning, governing the bidder’s competitiveness.  
 
Bidding trends over time  
Much has been reported on variations in bids over time, suggesting that price differences 
originate in systematic variations rather than random variations (e.g. de Neufville et al. 1977, 
Flanagan and Norman 1985, Chan et al. 1996). Runeson and Skitmore (1999) argued that 
variations in bids over time can be explained by changes in demand, firm capacity level and 
competitor behaviour. Highly correlated with changes in demand is the bidders’ need for 
work, which tends to be high in recession time as demand decreases. As demonstrated by 



 
Australasian Journal of Construction Economics and Building 

Soo,  A  and  Oo,  B  L  (2010)  ‘The  effect  of  information  feedback  in  construction  bidding’, Australasian  Journal  of 
Construction Economics and Building, 10 (1/2) 65‐75  

68 

McCaffer and Pettitt’s (1976) cusum curve, bidders typically reduce their bids gradually, 
relative to their competitors, until they win a contract. The attainment of the contract leads to 
a sharp increase in the subsequent bids by the bidders as they were not so eager to win a 
job. In their analysis using around 600 contracts involving about 400 contractors, it appears 
that there is a continuous cycle of upward and downward bidding trends over time. However, 
in a further study by Skitmore and Runeson (2006) that aimed at testing the statistical 
significance of the bidding trends detected in McCaffer and Pettitt (1976), they found that: (i) 
winning bids are not in general preceded by increasingly more competitive bids, and (ii) the 
trend of high and low bids over a period of time is most likely due to the presence of highly 
uncompetitive bids or outliers, for their dataset comprised bids from eight bidders. In yet 
another paper, Oo and Lo (2010) have identified the specific types of bidding trends before 
and after a winning bid using the longest series of bids from 67 bidders. Their model 
parameter estimates support the upward and downward trends before and after a winning 
bid in McCaffer and Pettitt’s cusum curve (1976).  
 
Here, we are concerned with changes in bidders’ pricing behaviour over time in response to 
two information feedback conditions. As Runeson and Skitmore (1999) conjecture, the 
assumption that competing bidders do not modify their behaviour at any time (i.e., no 
allowance for continuity) is unlikely to be valid. The strategic motivation for changes in 
pricing behaviour is the long-term survival of a firm (Skitmore and Smyth 2007). Many 
construction organizations feel they have to fight for survival (Skitmore et al. 2006), 
especially when demand levels lead to overcapacity, intensified competition or changing 
client needs (Skitmore and Smyth 2007). With this as a backdrop, contractors have to rely 
on effective pricing methods in order to translate potential business into reality. This 
inevitably involves effective utilisation of bidding feedback information towards winning jobs 
with high profit potential. This study follows the notion that information never has a negative 
value to the decision-maker (Milgrom and Weber 1982). At worst, irrelevant bidding 
feedback information can be ignored by the bidders. Based on the research aim and 
objectives, there are two hypotheses that form the foundation of empirical investigation in 
this paper: 
 

H1: Inexperienced bidders do change their pricing behaviour in response to a given 
set of bidding feedback information.  
 
H2: Inexperienced bidders with full bidding feedback information are more 
competitive than inexperienced bidders with limited bidding feedback information in 
their bidding attempts. 

 
Research Method  
An experimental approach was chosen as the most suitable method to test the effects of 
information feedback conditions in construction bidding. Given that there are so many 
possible factors affecting contractors’ decision making in pricing - only an experimental 
research design would allow for manipulation of variable(s), something that would not have 
been possible using field data. The complete design characteristics that apply to the 
experiment presented next were detailed in Oo and Soo (2010).  
 
The experiment involved final year undergraduate students with a construction project 
management background and were enrolled in a project procurement course. They were 
randomly split into two groups, one with full bidding feedback information (list of all bidders 
and their submitted bids), the other with partial bidding feedback information (winning bid 
and identity of winning bidder). The two primary groups were further split into five subgroups 
(4 students in each subgroup) to emulate a bidding competition of five competing bidders. It 
should be noted that the experiment was conducted in a controlled environment with an 
experiment coordinator ensuring that strictly no communication was allowed between the 
groups to prevent bidder collusion. 



 
Australasian Journal of Construction Economics and Building 

Soo,  A  and  Oo,  B  L  (2010)  ‘The  effect  of  information  feedback  in  construction  bidding’, Australasian  Journal  of 
Construction Economics and Building, 10 (1/2) 65‐75  

69 

The experiment was carried out for eight rounds (one round per week) where seven 
hypothetical projects were generated in each bidding round. All bidding feedback information 
(either partial or full) was provided at the beginning of each bidding round. Within a 
timeframe of one hour, the subjects were required to decide which project to bid for, and the 
bid price if deciding to bid. The general instruction to the participants was that their ultimate 
aim was to survive and prosper, in a competition in which the lowest bidder wins the job, but 
how this can be achieved was left to them. This reflects the strategic nature of the 
construction pricing problem. The hypothetical projects were constructed using information 
from past real contracts obtained from the NSW e-tendering website. The projects selected 
were conventional buildings, such as schools and institutional buildings that involve usual 
design, and do not require any unusual construction technologies. This was done to control 
the effect of project type on bidders’ bidding decisions. Apart from the project information 
(location, duration, client and contract type), the subjects were also given an unbiased cost 
estimate for each hypothetical project, which is the net project construction cost that includes 
the site overheads and project preliminaries (i.e., total of direct cost estimate + site 
overheads). Here, identical hypothetical projects were given to both the partial and full 
information feedback groups to enable direct comparison. In an attempt to make the 
experiment more realistic and to maintain subjects’ interests over eight rounds, profit/loss 
was generated for each hypothetical project by deducting a randomly assigned final cost 
from the winning bid. The subject who generated the biggest profit at the end of eight rounds 
was declared the winner and received a mystery prize. 
 
The main limitations of this experiment are that: (i) it is utilising students as experimental 
bidding subjects, and (ii) it does not consider other possible information feedback conditions. 
However, it is believed that the student subjects in the experiment have responded seriously 
since they asked the relevant questions after reading the instructions. In addition, the 
mystery prize for winning bidder was introduced as an incentive for the subjects to perform 
seriously in the experiment. In fact, the use of students in bidding experiments is a common 
approach to examine the effects of various bidding variables on bidders’ bidding behavour 
(e.g. Dufwenberg and Gneezy 2002; Issac and Walker 1985; Neugebauer and Perote 2008). 
A study by Dyer et al. (1989) compared naïve (student) bidders against experienced 
(company executives) bidders in an experimental construction bidding environment. They 
found that there is no significant difference between the naïve and experienced bidders in 
terms of bidding performance. They did however note that naïve and experienced bidders 
differ in terms of their risk attitudes with naïve bidders being more risk aversive as opposed 
to risk neutral. With regards to the other limitation of other possible information feedback 
conditions, it is feasible to replicate the experiment with (i) no information feedback condition, 
and (ii) information feedback on winning bid only.  
 
The data collected from this experiment was analysed using the Statistical Package for the 
Social Sciences (SPSS). The measure of competitiveness between bids is to express each 
bid as a ratio above the unbiased cost estimate, i.e., mark-up competitiveness ratio, MUCR 
= bidder’s bid/unbiased cost estimate. The unbiased cost estimate provides a common 
baseline for comparison between the partial and full information feedback groups. A MUCR 
of 1 indicates that a bid is at the unbiased cost estimate (i.e., zero mark-up), and below 1 
indicates a bidder has submitted a bid lower than the unbiased cost estimate that leads to a 
lower bid. Lower MUCR values indicate greater competitiveness since the lowest bidder 
wins at the lowest bid price. Consistency in bidding was then gauged from the resultant 
standard deviation. 
 
Results and Discussion 
The results are presented in three parts. The first part presents an exploratory analysis of 
bids obtained from both the partial and full information feedback groups. The 
competitiveness ratio (MUCR) was plotted against the corresponding bidding rounds to 
explore the differences in bidding trend between the two groups. The second part discusses 



 
Australasian Journal of Construction Economics and Building 

Soo,  A  and  Oo,  B  L  (2010)  ‘The  effect  of  information  feedback  in  construction  bidding’, Australasian  Journal  of 
Construction Economics and Building, 10 (1/2) 65‐75  

70 

the results of subseries analysis in detecting changes in bidding behaviour over time. The 
third part details the test results of the effects of bidding feedback information on the bidders’ 
bidding competitiveness.  
 
Exploratory Analysis 
Table 1 shows the sample size for both the partial and full information feedback groups. For 
the partial information feedback group (i.e., Bidders A1 to A5), a total of 261 bids were 
obtained with the other 14 representing outliers or no-bid decisions. The full information 
feedback group (i.e., Bidders B1 to B5) provided 249 bids with 26 bids representing outliers 
and no-bid decisions. The removal of outliers was based on criterion set forth by the Hong 
Kong SAR government which considers all bids that are 25% above the lowest bid to be non-
serious bids (Skitmore 2002). An one-sample Kolmogorov-Smirnov (K-S) test on the two 
data samples reveals a p-value less than 0.05, indicating a violation of the assumption of 
normal distribution. Thus, non-parametric tests were used for subsequent testing of 
hypotheses.  
 
 Bid  No-bid/Outlier Total 

Feedback Conditions N Percent N Percent N Percent 

Partial Information 261 94.9% 14 5.1% 275 100.0% 

Full Information 249 90.5% 26 9.5% 275 100.0% 

Table 1 Sample size for partial and full information feedback groups 
 
 
For the exploratory analysis, scatter plots were used to display the spread of data for the 
partial and full information feedback groups as shown in Figures 1(a) and 1(b), respectively. 
LOWESS curves fitted to the scatter plots allow for bidding trends to be observed from the 
dataset. LOWESS stands for "LO"cally "WE"ighted "S"catter plot "S"moothing (Hardle 1990). 
The LOWESS fit line is based on local polynomial least squares fit to a set of data points. 
The fit is then "robustified" which is defined as resmoothing of the curve for several iterations 
to provide a more accurate trend representation of the dataset (Hardle,1990). The robustified 
LOWESS curve is more resistant to effects of noise and/or marginal outliers in any particular 
dataset. It can be seen that there is a steep decreasing trend in MUCR (i.e., more 
competitive bids) for the partial information feedback group from Round 1 to 2. This can be 
considered as a learning curve adjustment. In this, with the winning bids revealed to all 
bidders, the bidders were able to assess their bids relative to the winning bids, and to review 
their performance and adjust their bidding strategies accordingly. However, from Round 3 
onwards, the curve begins to stabilise, possibly representing the bidders’ optimum bidding 
trend. It should be noted that the profit and loss statements provided to the bidders were not 
available until the end of Round 2 since the hypothetical projects have minimum project 
duration of two rounds. However, the feedback on profit/loss on winning bids did not trigger 
an immediate response in bidding trend. This observation is explained in Fu et al. (2004) 
where contractors becoming experienced through recurrent bidding and having obtained the 
optimal level of bidding strategy. Pawlowsky (2001) suggests that the steady state phase in 
bidding trend is because of the balance between the market forces and the knowledge of 
sustaining optimal bidding strategy. He advocates that a behavioural regularity is a symbol of 
best practices that were developed from a survival need to stay in the market and to make a 
profit. However, as Fu et al. (2004) pointed out, when the market forces change, learning 
occurs again and bidders will find alternative strategies to adapt to the new environment. 
This may explain the slight decreasing trend in MUCR for the last few rounds of the 
experiment for the partial information feedback group.  
   



 
Australasian Journal of Construction Economics and Building 

Soo,  A  and  Oo,  B  L  (2010)  ‘The  effect  of  information  feedback  in  construction  bidding’, Australasian  Journal  of 
Construction Economics and Building, 10 (1/2) 65‐75  

71 

            (a) Partial information feedback group              (b) Full information feedback group  

 
                    
 

Figure 1 MUCR scatter plots for partial and full information feedback groups 
 
For the full information feedback group, there is a continually decreasing trend in the MUCR, 
leading to generally lower bids. The LOWESS curve shows a fairly "flat" line, suggesting a 
consistent bidding trend among the five bidders. It appears that the full bidding feedback 
information throughout the experiment did not trigger an immediate response in bidding 
behaviour among the bidders. This could be due to the fact that the bidders had made full 
use of the bidding feedback information (identity and bids from all competing bidders) and 
were able to bid consistently and competitively. Another possible explanation for the rather 
consistent trend is that the extensive bidding feedback information retards the bidders’ 
responses (learning) to new situations (Kagel and Levin 2002). The full information feedback 
group in the experiment have sufficient information to formulate their bidding strategies, and 
thus there is less reliance on experience and learning gained through recurrent bidding.  
 
Comparing the two groups, it can be observed from the LOWESS curves that the full 
information feedback group bid more consistently and with lower overall bids compared to 
the partial bidding feedback information group. These are denoted by the LOWESS curves 
for partial information feedback group being above MUCR of 1 and the full information 
feedback group having the curve mostly below 1. The latter suggests that Bidders B1 to B5 
in full information feedback group had bid aggressively in general with tiny or even negative 
mark-ups (see Figure 1(b)). The amount of "kinks" in Figure 1(a) for the partial information 
feedback group also represents a lower bidding consistency, which is probably indicative of 
the adjustment in bidding strategies among Bidders A1 to A5. 
 
Changes in Bidding Trend 
The subseries analysis in Skitmore and Runeson (2006) was adopted here to test the 
hypothesis on changes in bidding trend over time. The disjointedness observed in the 
LOWESS curves over the eight bidding rounds was used as a basis to divide all bids for 
each information feedback group into subseries. A Friedman test was then applied to the 
multiple related subseries of bids for checking statistically significant differences in bidding 
trends. 



 
Australasian Journal of Construction Economics and Building 

Soo,  A  and  Oo,  B  L  (2010)  ‘The  effect  of  information  feedback  in  construction  bidding’, Australasian  Journal  of 
Construction Economics and Building, 10 (1/2) 65‐75  

72 

From the LOWESS curves in Figures 1(a) and 1(b), it can be seen that there are three 
disjointed trends for both the partial and full information feedback groups, indicating three 
subseries that comprised bids from Round 1 to 2, 3 to 4 and 5 to 8. The Round 2 disjoint 
could possibly be due to the release of profit and loss statements to the bidders’ (triggering a 
behavioural response), however the Round 5 disjoint common to both information feedback 
conditions may be purely coincidental. The mean ranks of each subseries were then 
calculated, and the Chi-square values found in Friedman test are reported in Table 2. It 
should be noted that the mean rank is different to an arithmetic mean, and is an average of 
the MUCR rank with respect to the round subseries. A higher mean rank for a particular 
subseries indicates that the subseries has a higher MUCR overall. Consistent with the 
decreasing trends in LOWESS curves, the results show that the mean rank for Round 1 to 2 
subseries is higher than the subsequent two subseries in both information feedback groups. 
However, it is clear that there is no statistically significant difference in MUCR for the three 
related subseries for the partial information feedback group (p = 0.403). Despite the “kinks” 
in the respective LOWESS curve, the results indicate that the variations in bids over time in 
response to the given set of partial bidding feedback information are statistically insignificant. 
This seemingly stable trend indicated by the respective mean rank can partly be explained 
because the bidders did not attempt “aggressive” manoeuvres such as bidding at or below 
the unbiased cost estimate (MUCR remained above 1.0 at all times).  
  
In considering the full information feedback group, the results show that there are significant 
differences in MUCR across the three related subseries at the conventional 0.05 cut-off 
level. There is evidence to support that the bidders do modify their pricing behaviour based 
on the given set of full bidding feedback information. The respective mean rank indicates a 
rather steep decreasing trend in MUCR over the bidding periods, suggesting considerable 
changes in the bid price in terms of both frequency and magnitude. This observation is 
similar to that of Neugebauer and Selten (2006). They found that bidders adjusted their bids 
more frequently when they received clear information on their competitors’ bids. Here, the 
hypothesis H1 which states that “inexperienced bidders do change their pricing behaviour 
systematically in response to a given set of bidding feedback information” is considered 
supported. It appears that learning does occur for both the partial and full information 
feedback groups but at different rates, with increasing accuracy and consistency in bid 
prices over the respective three subseries (i.e., decreasing differences in MUCR). 
 

Round Subseries Mean Rank N Chi-Square df p 

Partial information feedback group    

1 - 2 2.133 60 1.816 2 0.403 

3 - 4 1.975     

5 - 8 1.892     

Full information feedback group   

1 - 2 2.542 59 27.526 2 0.000 

3 - 4 1.822     

5 - 8 1.636     

Table 2 Friedman test results for partial and full information feedback groups 
 
The Effects of Information Feedback Conditions on Bidding Competitiveness 
Table 3 shows the means of MUCR for the partial and full information feedback groups. It 
can be seen that the full information feedback group is more competitive on average with a 
lower mean MUCR compared to those in partial information feedback group. In addition, the 
standard deviation for the full information feedback group is lower, indicating a higher degree 
of consistency in their bidding attempts.  



 
Australasian Journal of Construction Economics and Building 

Soo,  A  and  Oo,  B  L  (2010)  ‘The  effect  of  information  feedback  in  construction  bidding’, Australasian  Journal  of 
Construction Economics and Building, 10 (1/2) 65‐75  

73 

 N Mean MUCR Std. Deviation 

Partial Information 261 1.0197 0.0361 

Full Information 249 1.0057 0.0284 

Table 3 Comparison of means of MUCR 
 
To validate hypothesis H2, a Mann-Whitney U test was performed to test the difference in 
bidding competitiveness in terms of MUCR between the two groups. The results shows that 
the mean MUCR for full information feedback group is lower than the partial information 
feedback group at p < 0.05 (U = 19356.6; Z = -7.899; p = 0.000). Hypothesis H2 is thus 
supported where inexperienced bidders with full bidding feedback information are more 
competitive than those with partial bidding feedback information. This suggests that, in 
construction bidding, a full information feedback condition would lead to lower average bids. 
The conjecture to explain the findings is that comprehensive bidding feedback information 
opens up an "avenue" from which bidders may use the bidding data to inform their bidding 
decisions, and thus become more competitive and consistent in their bidding attempts. This 
conjecture is further supported by a paperback survey of the subjects with full bidding 
feedback information. The survey was allotted in every bidding round and the subjects were 
asked to indicate the usage of bidding data. For the eight bidding rounds there is a response 
rate of 90% whom had all indicated that they had utilised the bidding feedback information in 
formulating their bidding strategies and bid prices.  
 
In examining the means of MUCR further, it is clear that the percentage mark-ups for both 
the partial and full information feedback groups are relatively low. While the partial 
information feedback group had a mean percentage mark-up of 1.97, a mean percentage 
mark-up as low as 0.57 was recorded for the full information feedback group. The general 
impression here is that the bidders had submitted bids with tiny or even negative mark-ups in 
the experiment, and that the resultant bids would likely result in a loss. In this case, the 
majority of the bidders in both groups recorded a loss at the end of experiment, with several 
instances of ‘suicidal’ bids that resulted in big losses. Although the phenomenon of 
submitting suicidal bids is not new in literature (e.g. Fellows and Langford 1980; Dyer and 
Kagel 1996), it would seem that the bidders’ bidding behaviour in the experiment is affected 
by loss aversion to different degrees. However, the observed bidding trend shall not nullify 
the hypothesis testing results since the method of assigning final cost for the hypothetical 
projects is purely random.  
 
Conclusions 
This research examines the effects of information feedback conditions in construction 
bidding through an experimental setting, where two groups of inexperienced bidders were 
supplied with full and partial bidding feedback information, respectively. The results show 
that the variations in bids over time in response to a given set of full bidding feedback 
information are statistically significant, but not for bids from bidders with access to partial 
bidding feedback information. Hypothesis H1 is thus considered partially supported. The test 
results for hypothesis H2 show that inexperienced bidders with full bidding data from 
previous bidding rounds are more competitive than those with partial bidding feedback 
information. The mean mark-up competitiveness ratio (i.e., bidder’s bid/unbiased cost 
estimate) for the full information feedback group is 0.014 lower than the partial information 
feedback group. This suggests that full information feedback condition would lead to lower 
average bids in construction bidding. The findings add to both our theoretical and empirical 
understanding of construction bidding: an understanding of the process of changes in the 
price of building work, and how the process can be manipulated through the release of 
bidding feedback information.  
 



 
Australasian Journal of Construction Economics and Building 

Soo,  A  and  Oo,  B  L  (2010)  ‘The  effect  of  information  feedback  in  construction  bidding’, Australasian  Journal  of 
Construction Economics and Building, 10 (1/2) 65‐75  

74 

For construction clients, this study has demonstrated the need to consider the level of 
bidding feedback information in their formulation of procurement strategies. However, of the 
little empirical evidence in literature, further work is required to determine the point at which 
bidding feedback information can be regarded as ‘sufficient’ to aid efficiency in construction 
bidding. It may be possible to replicate this study by having experienced bidders as subjects 
to obtain empirical support on the extent to which contractors’ bid pricing is affected by 
varying information feedback conditions, and thus to establish the external validity of the 
experiment. For further testing, an alternative would be to apply a statistical modelling 
technique, say, mixed effects model to provide estimation for bidding trends over time in 
response to different information feedback conditions 
 
Acknowledgements 
The authors would like to thank Raymond Loh for his assistance in experimental design as 
well as collecting the bidding data for the experiment. 
 
References 
Chan, SM, Runeson, G and Skitmore, M (1996) ‘Changes in profit as market conditions 
change: An historical study of a building firm’, Construction Management and Economics, 
14(3), 253-264 
De Neufville, R, Hani, EN and Lesage, Y (1977) ‘Bidding models: effects of bidders' risk 
aversion’, Journal of the Construction Division, ASCE, 103, 57-70 
De Silva, DG, Dunne, T, Kankanamge, A and Kosmopoulou, G (2008) ‘The impact of public 
information on bidding in highway procurement auctions’, European Economic Review, 52 
(1), 150-181 
De Silva, DG, Kosmopoulou, G and Lamarche, C (2009) ‘The effect of information on the 
bidding and survival of entrants in procurement auctions’, Journal of Public Economics, 93 
(1-2), 56-72 
Drew, DS and Skitmore, M (1992) ‘Competitiveness in bidding: a consultant's perspective’, 
Construction Management and Economics, 10 (3), 227-247 
Drew, DS, and Fellows, RF (1996) ‘Feedback in construction contract bidding’, Construction 
Modernization and Education: Proc., CIB Int. Conf., Oct 21–24, WP. Chang, ed., China 
Architectural & Building Press, Beijing  
Dufwenberg, M and Gneezy, U (2002) ‘Information disclosure in auctions: an experiment’, 
Journal of Economic Behavior and Organization, 48 (4), 431-444 
Dyer, D, Kagel, JH and Levin, D (1989) ‘A Comparison of Naive and Experienced Bidders in 
Common Value Offer Auctions - A Laboratory Analysis’, Economic Journal, 99 (394), 108-15 
Dyer, D and Kagel, JH (1996) ‘Bidding in common value auctions: how the commercial 
construction industry corrects for the winners’ curse’, Management Science 42 (10), 1463–
1475 
Engelbrecht-Wiggans, R and Katok, E (2008) ‘Regret and feedback information in first-price 
sealed-bid auctions’, Management Science, 54 (4), 808-19 
Esponda, I (2008) ‘Information feedback in first price auctions’, Rand Journal of Economics, 
39 (2), 491-508 
Fellows, RF and Langford, DA (1980) ‘Decision theory and tendering’, Building Technology 
and Management, Oct, 36-39 
Flanagan, R and Norman, G (1985) ‘Sealed bid auctions: an application to the building 
industry’, Construction Management and Economics, 3 (2), 145- 61 
Friedman, L (1956) ‘A competitive bidding strategy’, Operations Research, 4, 104-112 



 
Australasian Journal of Construction Economics and Building 

Soo,  A  and  Oo,  B  L  (2010)  ‘The  effect  of  information  feedback  in  construction  bidding’, Australasian  Journal  of 
Construction Economics and Building, 10 (1/2) 65‐75  

75 

Fu, WK, Drew, DS and Lo, HP (2004) ‘Start-up and steady-state learning in recurrent 
bidding’, Building Research and Information, 32 (6), 484-496.Hardle, W (1990) Applied 
Nonparametric Regression, Oxford University Press, Cambridge 
Isaac, RM and Walker, JM (1985) ‘Information and conspiracy in sealed bid auctions’, 
Journal of Economic Behavior and Organization, 6 (2), 139-159 
Kagel, JH and Levin, D (2002) Common Value Auctions and the Winner's Curse, Princeton 
University Press, New Jersey 
Kerzner, H (2006) Project Management - a Systems Approach to Planning, Scheduling and 
Controlling, John Wiley & Sons, New Jersey 
Kortanek, KO, Soden, JV and Sodaro, D (1973) ‘Profit analyses and sequential bid pricing 
models’, Management Science Series A-Theory, 20 (3), 396-417 
McCaffer, R (1976) Contractors’ Bidding Behaviour and Tender Price Prediction, 
unpublished PhD thesis, Loughborough University of Technology 
McCaffer, R and Pettitt, AN (1976) ‘Bidding behaviour in project management’, The Project 
Manager, 1 (5), 5–8 
Milgrom, P (1989) ‘Auctions and bidding - a primer’, Journal of Economic Perspectives, 3 (3), 
3-22 
Milgrom, P and Weber, RJ (1982) ‘The value of information in a sealed-bid auction’, Journal 
of Mathematical Economics, 10(1), 105-114 
Ministry of Finance (2005) Contracts and Purchasing Procedures, Ministry of Finance, 
Singapore 
Neugebauer, T and Perote, J (2008) ‘Bidding 'as if' risk neutral in experimental first price 
auctions without information feedback’, Experimental Economics, 11 (2), 190-202 
Neugebauer, T and Selten, R (2006) ‘Individual behavior of first-price auctions: The 
importance of information feedback in computerized experimental markets’, Games and 
Economic Behavior, 54 (1), 183-204 
New South Wales Government (2005) Code of Practice for Procurement, New South Wales 
Government, Australia 
Ockenfels, A and Selten, R (2005) ‘Impulse balance equilibrium and feedback in first price 
auctions’, Games and Economic Behavior, 51 (1), 155-170 
Oo, BL and Lo, HP (2010) The effect of bidding success on contractors’ bidding behaviour, 
Journal of Construction, Engineering and Management ASCE, (under review) 
Oo, BL and Soo, A (2010) ‘Bidding experiment: testing the effects of learning and 
information feedback in construction bidding’, AUBEA 2010 Conference, Melbourne 
Pawlowsky, P (2001) The treatment of organizational learning in management science. 
Handbook of Organizational Learning and Knowledge, Oxford University Press, New York 
Runeson, G (2000) Building Economics, Deakin University Press, Geelong 
Runeson, G and Skitmore, M (1999) ‘Tendering theory revisited’, Construction Management 
and Economics, 17 (3), 285-296 
Skitmore, M (2002) ‘Identifying non-competitive bids in construction contract auctions’, 
Omega, 30 (6), 443-449 
Skitmore, M and Runeson, G (2006) ‘Bidding models: testing the stationarity assumption’, 
Construction Management and Economics, 24 (8), 791-803 
Skitmore, M and Smyth, HJ (2007) ‘Pricing construction work: a marketing viewpoint’, 
Construction Management and Economics, 25 (6), 619–630 
Skitmore, RM Runeson, G and Chang, X (2006) ‘Construction price formation: full-cost 
pricing or neoclassical microeconomic theory?’, Construction Management and Economics, 
24 (7), 773–784 


	The effect of information feedback in construction bidding
	Abstract
	Introduction
	Effects of Information in Construction Bidding 
	Public Information
	Feedback Information

	Bidding trends over time 
	Research Method 
	Results and Discussion
	Exploratory Analysis
	Changes in Bidding Trend
	The Effects of Information Feedback Conditions on Bidding Competitiveness

	Conclusions
	Acknowledgements
	References