Predicting Profit Performance for Selecting Candidate International Construction Projects

Han, Seung Heon; Kim, Du Y.; Kim, Hyoungkwan

doi:10.1061/(asce)0733-9364(2007)133:6(425)

Cited by 79 publications

(40 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This dimension influences Co-S, as generating sufficient profit is important for a contractor's continued survival. Profitability is the gain or loss divided by the total contract amount (Han et al, 2007). Also important to contractors is profit optimization, obtained by identifying the amount and timing of the periodical inflow and outflow of resources as projects progress (Liu and Wang, 2009).…”

Section: Profitabilitymentioning

confidence: 99%

Determinants of contractor satisfaction

Masrom

Skitmore

Bridge

2013

Construction Management and Economics

View full text Add to dashboard Cite

Performance in the construction industry is increasingly scrutinized as a result of the delays, cost overruns and poor quality of the indusuy's products and services. Increasingly, disputes, conflicts and mismatches of objectives among participants are contributory factors. Performance measurement approaches have been developed to overcome these problems. However, these approaches focus primarily on objective measures to the exclusion of subjective measures, particularly those concerning contractor satisfaction (Co-S). The contractor satisfaction model (CoSMo) developed here is intended to rethe situation. Data derived kom a questionnaire survey of 75 large contractors in Malaysia in respect of a key project are analysed to identify participant factors and their strength of relationship with Co-S dimensions. The results are presented in the form of eight regression equations. The outcome is a tool for use by project patticipants to provide a better understanding of how they, and the project, affect contractor satisfaction. The developed model sheds some light on a hitherto unknown aspect of construction management in providing an increased awareness of the importance of major Malaysian construction contractors' needs in the execution of successful projects.

show abstract

Section: Profitabilitymentioning

confidence: 99%

Determinants of contractor satisfaction

Masrom

Skitmore

Bridge

2013

Construction Management and Economics

View full text Add to dashboard Cite

show abstract

“…However, such assessment is usually plagued by ambiguousness from maintaining consistency with different standards depending on expert's cognitive limitations or the cooperation cultures during surveys. In addition, input data usually include subjective assessments that often distort practitioner's decision-making and judgment [15,25]. As such, previous studies that have attempted to classify profit levels by Likert or numeric scale have not yet reached a statistical level of significance in profit prediction due to their compound mechanism and a large number of inputs for understanding complicated project information [15,26].…”

Section: Research Backgroundmentioning

confidence: 99%

Naïve Bayesian Classifier for Selecting Good/Bad Projects during the Early Stage of International Construction Bidding Decisions

Jang

Lee

et al. 2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Since the 1970s, revenues generated by Korean contractors in international construction have increased rapidly, exceeding USD 70 billion per year in recent years. However, Korean contractors face significant risks from market uncertainty and sensitivity to economic volatility and technical difficulties. As the volatility of these risks threatens project profitability, approximately 15% of bad projects were found to account for 74% of losses from the same international construction sector. Anticipating bad projects via preemptive risk management can better prevent losses so that contractors can enhance the efficiency of bidding decisions during the early stages of a project cycle. In line with these objectives, this paper examines the effect of such factors on the degree of project profitability. The Naïve Bayesian classifier is applied to identify a good project screening tool, which increases practical applicability using binomial variables with limited information that is obtainable in the early stages. The proposed model produced superior classification results that adequately reflect contractor views of risk. It is anticipated that when users apply the proposed model based on their own knowledge and expertise, overall firm profit rates will increase as a result of early abandonment of bad projects as well as the prioritization of good projects before final bidding decisions are made.

show abstract

“…The major risk factors are identified in Table 2 using the most influential profitability impact factors given in the previous section. The list of 66 elements of the business-version PDRI was originally identified and categorized according to 10 references [37][38][39][40][41][42][43][44][45][46][47]. The elements were weighted in order of importance using the inputs from 15 experienced project managers and estimators who each have between 10 and 20 years of experience.…”

Section: Trizmentioning

confidence: 99%

A Probabilistic Alternative Approach to Optimal Project Profitability Based on the Value-at-Risk

Kim

2018

Sustainability

View full text Add to dashboard Cite

This paper focuses on an investment decision-making process for sustainable development based on the profitability impact factors for overseas projects. Investors prefer to use the discounted cash-flow method. Although this method is simple and straightforward, its critical weakness is its inability to reflect the factor volatility associated with the project evaluation. To overcome this weakness, the Value-at-Risk method is used to apply the volatility of the profitability impact factors, thereby reflecting the risks and establishing decision-making criteria for risk-averse investors. Risk-averse investors can lose relatively acceptable investment opportunities to risk-neutral or risk-amenable investors due to strict investment decision-making criteria. To overcome this problem, critical factors are selected through a Monte Carlo simulation and a sensitivity analysis, and solutions to the critical-factor problems are then found by using the Theory of Inventive Problem Solving and a business version of the Project Definition Rating Index. This study examines the process of recovering investment opportunities with projects that are investment feasible and that have been rejected when applying the criterion of the Value-at-Risk method. To do this, a probabilistic alternative approach is taken. To validate this methodology, the proposed framework for an improved decision-making process is demonstrated using two actual overseas projects of a Korean steel-making company.

show abstract

Predicting Profit Performance for Selecting Candidate International Construction Projects

Cited by 79 publications

References 19 publications

Determinants of contractor satisfaction

Determinants of contractor satisfaction

Naïve Bayesian Classifier for Selecting Good/Bad Projects during the Early Stage of International Construction Bidding Decisions

A Probabilistic Alternative Approach to Optimal Project Profitability Based on the Value-at-Risk

Contact Info

Product

Resources

About