| Summary: | Construction industry is a competitive industry and the only possible way for a contractor to survive is by winning the tenders and making profit. Therefore, a "right" mark-up size is essential for contractor which to maximum possible profit, at the same time keeping its bid at a competitive level. Hence possessing a sound knowledge of the factors affecting the contractors' mark-up size decision is imperative in identifying the "right" mark-up size in bidding. Thus, this project is to investigate the factors affecting the mark-up size decision. It seeks to determine the factors affecting the mark-up size decision and analyzes the perceived importance of various factors in different contractor size's evaluation. The project extent investigates on the current practices in contractors' mark-up size decision. Questionnaire conducted and distributed to the respondents who are the medium and large-size of contractors in Johor Bahru, Malaysia. This finding of project shows that there are top ten important factors affecting mark-up size decision such as overall economy, competition, need for work, size of project, project cash flow, and so on. Besides, the ranking of most influence category of factor were followed by project characteristics, company characteristics, economic situation, project documentation and bidding situation. Finding also indicates that the different of perceived important of factors between medium and large-size contractors' evaluation. Seven factors which are degree of difficulty, uncertainly in cost estimate, need for work, availability of qualified staff, time allowed submitting bids, bidding document price and risk involved in investment has been highlighted. The finding shows that the most preference mark-up size is 10 % to 15%. Experience, previous record and market survey are commonly practiced by contractors in determining their mark-up size. Unfortunately, the bidding models were not utilized by contractors since they are not sufficient information to effectively use it and the complexity of these models.
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