The construction industry lags behind other industries in adopting new information technologies (ITs). The current construction procedures used to transfer data need to be updated. The most common data capture technologies are bar code and radio frequency identification (RFID) tagging. Although bar code and RFID systems are quite different, they might accomplish the same tasks.;In fact, so many new data capture technologies now exist that industry managers are often perplexed when they plan a new system. This research develops a decision tool that enables decision makers in the construction industry to select the most appropriate data capturing technology for their construction applications. This decision tool is a systematic evaluation model based upon the Multi-Attribute Utility theory (MAUT).;This Multi-Attribute Utility Model (MAUM) can be adapted to evaluate other construction-related applications such as construction equipment, building methods, and new projects. MAUM is also a good group decision-making tool that considers all decision makers' concerns and objectives.;MAUM's feasibility as a decision tool was assessed by both laboratory technicians and information technology professionals (ITPs) at six material testing labs, both private and government. Aggregate utilities were calculated for 10 different bar code and RFID portable data terminals (PDTs). These aggregate utilities simultaneously combine technical-merit, economic-merit, and low-risk merit utilities for the PDTs. Two RFID and one bar code systems achieved the greatest aggregate utilities, indicating that some bar code systems can be better than other RFID systems.;There were not many significant differences between government and private labs, although private labs are profit oriented and thus more cost conscious, making them more selective in systems technical performance. The study showed that ITPs are more concerned about technical specifications of the PDTs, while technicians are more concerned about its environmental reliability. Sensitivity analysis revealed that changes in model interaction relationships, system cost, and the aggregation rules had no major effect on the ranking of top systems.