Goal hierarchy: Improving asset data quality by improving motivation

Many have recognized the need for high quality data on assets and the problems in obtaining them, particularly when there is a need for human observation and manual recording. Yet very few have looked at the role of the data collectors themselves in the data quality process. This paper argues that there are benefits to more fully understanding the psychological factors that lay behind data collection and we use goal hierarchy theory to understand these factors. Given the myriad of potential reasons for poor-quality data it has previously proven difficult to identify and successfully deploy employee-driven interventions; however, the goal hierarchy approach looks at all of the goals that an individual has in their life and the connections between them. For instance, does collecting data relate to whether or not they get a promotion? Stay safe? Get a new job? and so on. By eliciting these goals and their connections we can identify commonalities across different groups, sites or organizations that can influence the quality of data collection. Thus, rather than assuming what the data collectors want, a goal hierarchy approach determines that empirically. Practically, this supports the development of customized interventions that will be much more effective and sustainable than previous efforts.

► We need to consider psychological aspects of data collectors to improve data quality.
► We show how goal hierarchy theory furthers understanding.
► Looks at the multiple goals of each individual to determine their behavior.