Despite the popularity of motivation research in the latter half of the twentieth century, little empirical evidence exists of the factors influencing the motivation of different occupational groups within the construction industry. Furthermore, no significant attempt has been made to compare the job motivation level difference of the employees working in large companies or small-medium sized enterprises (SMEs). This research attempts to fill these knowledge gaps by exploring the motivation of members of three occupational groups (professional engineers, skilled trade-craft workers and unskilled or semi-skilled general operatives), working for a variety of SMEs and large sized businesses. The findings reveal that professional employees are predominantly motivated by intrinsic reward, which contrasts markedly with unskilled workers who demonstrate a desire for extrinsic rewards. However, company size does not appear to affect the motivation of any of the groups studied significantly.

Feature selection is an important task in many fields such as statistics and machine learning. It aims at preprocessing step that include removal of irrelevant and redundant features and the retention of useful features. Selecting the relevant features increases the accuracy and decreases the computational cost. Feature selection also helps to understand the relevant data, addressing the complexity of dimensionality. In this paper, we have proposed a technique that uses JRip classifier and association rule mining to select the most relevant features from a data set. JRip extracts the rules from a data set and then association rules mining technique is applied to rank the features. Twenty datasets are tested ranging from binary class problem to multi-class problem. Extensive experimentation is carried out and the proposed technique is evaluated against the performance of various familiar classifiers. Experimental results demonstrate that while employing less number of features the proposed method achieves higher classification accuracy as well as generates less number of rules.