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Abdelgawad, M and Fayek, A R (2010) Risk Management in the Construction Industry Using Combined Fuzzy FMEA and Fuzzy AHP. Journal of Construction Engineering and Management, 136(09), 1028–36.

Adriaanse, A, Voordijk, H and Dewulf, G (2010) Adoption and Use of Interorganizational ICT in a Construction Project. Journal of Construction Engineering and Management, 136(09), 1003–14.

Bayraktar, M E and Hastak, M (2010) Scoring Approach to Construction Bond Underwriting. Journal of Construction Engineering and Management, 136(09), 957–67.

Hallowell, M R and Gambatese, J A (2010) Population and Initial Validation of a Formal Model for Construction Safety Risk Management. Journal of Construction Engineering and Management, 136(09), 981–90.

Hwang, S (2010) Cross-Validation of Short-Term Productivity Forecasting Methodologies. Journal of Construction Engineering and Management, 136(09), 1037–46.

Hwang, S and Liu, L Y (2010) Contemporaneous Time Series and Forecasting Methodologies for Predicting Short-Term Productivity. Journal of Construction Engineering and Management, 136(09), 1047–55.

Ioannou, P G and Awwad, R E (2010) Below-Average Bidding Method. Journal of Construction Engineering and Management, 136(09), 936–46.

Love, P E D, Mistry, D and Davis, P R (2010) Price Competitive Alliance Projects: Identification of Success Factors for Public Clients. Journal of Construction Engineering and Management, 136(09), 947–56.

Sacks, R, Koskela, L, Dave, B A and Owen, R (2010) Interaction of Lean and Building Information Modeling in Construction. Journal of Construction Engineering and Management, 136(09), 968–80.

Serag, E, Oloufa, A, Malone, L and Radwan, E (2010) Model for Quantifying the Impact of Change Orders on Project Cost for U.S. Roadwork Construction. Journal of Construction Engineering and Management, 136(09), 1015–27.

Wong, J M W, Chan, A P C and Chiang, Y H (2010) Modeling Construction Occupational Demand: Case of Hong Kong. Journal of Construction Engineering and Management, 136(09), 991–1002.

  • Type: Journal Article
  • Keywords: Construction methods; Forecasting; Labor; Hong Kong; Construction skills; Exponential smoothing; Regression; Forecasting; Manpower demand;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000205
  • Abstract:
    Appropriate training can only be developed if training needs for specific skills are carefully identified. This paper, further to an aggregate model developed previously, aims to forecast the occupational share of the aggregate manpower demand for the construction industry of Hong Kong. The forecast, based on existing manpower statistics, is divided into two levels: broad occupations and detailed occupations. The broad occupational demand forecasting model is formulated using a time-series regression analysis to derive the relationship between the occupational share and the construction output cycle, technology, and various work-mix variables; whereas exponential smoothing technique is used to forecast the share of detailed occupations. This occupational demand estimation can provide solid information to facilitate manpower planning. It enables the policymakers to foresee the trends of occupational manpower demand and formulate policies and training and retraining programs tailored to deal effectively with the industry’s human resource requirements in this critical sector of the economy. Although the study focuses on developing models for the Hong Kong construction labor market, the adopted methodology can be applied in other labor markets to develop such models for manpower planning.

Zhao, Z Y, You, W Y and Zuo, J (2010) Application of Innovative Critical Chain Method for Project Planning and Control under Resource Constraints and Uncertainty. Journal of Construction Engineering and Management, 136(09), 1056–60.