should be careful of the degree to which "push" factors, such as managerial pressure and technological input control, are relied upon. While they may be helpful for motivating those data collectors who are not intrinsically motivated, they are either not helpful or may discourage those data collectors who are intrinsically motivated. Instead, self-concordance may act as a longer-term, more stable approach to increasing the motivation of data collectors and thus increasing the quality of data that enter reliability systems. This study uses a sequential mixed-method approach involving interviews with 20 data collectors and a quantitative survey of 109 data collectors in a water utility. It examines the interactive effect of managerial pressure, technological input control and self-concordance on data collection performance.The need to improve the quality of manually-acquired data on assets is wellknown in the reliability literature [1][2][3][4]. Manually-acquired data includes data gathered as a result of inspections, as a part of repair work, and during asset operation by personnel whose main role is to operate or maintain assets. These manually collected data are used, in conjunction with sensor data, to develop a picture of asset health and performance which informs decisions about asset renewals, repairs and replacements [5]. Recent integrations and critiques of the data quality literature [6,7] showed that although many proposed solutions to poor quality data have involved cleaning the data once they have been collected [8] many are also aimed at influencing the input of the data -this occurs through either changing external factors that influence the data collector such as managerial and technology structures [e.g., 4, 5], or changing the motivation of the data collector [e.g., 6, 9, 10]. As yet though, little empirical research has rigorously examined the effects of the factors affecting the input of the data. Given the importance of high quality data in reliability systems, and the cost of cleaning the data after their collection, this neglect is surprising. This research therefore empirically tests the effect of the most common three factors (the manager, the technology used in inputting the data, and the intrinsic motivation of the data collector), as well as examining the effect of the interplay between them on the quality of manual data collection.Although there has been considerable increase in the use of sensors and the volume of data collected by them, using operators and maintenance staff (data collectors) to collect data on assets is still a common practice. Manual data 2 collection leverages the experience of the data collector. It often requires them to provide an assessment of the asset's condition, identify a failure mode, or make a prediction as to remaining asset life in addition to recording observations or actions taken. However, when data collectors record their observations consideration needs to be given to psychological factors to ensure that appropriate factors are in place to encoura...