Quality of products and services is a key strategic variable for many organizations. A good understanding of quality improvement dirvers enables managers to better allocate resources to those areas most likely to improve quality levels. It can also lead to the redesign of management control systems to measure how progress on these variables is translated into improvement in quality levels._x000B__x000B_This paper examines quality improvement rates (QIR’s) in the electronics industry. One ojbective is to examine the extent to which learning rates can be explained by experience related variables (such as production volume). A second objective is to begin identifying factors (other than production volume) which influence the quality improvement rate. These factors are a starting point in building a richer theory of “quality learning” than is provided by existing production-volume based (“learning by doing”) models._x000B__x000B_The paper is arranged as follows: Section 1 presents a framework for different categories of quality improvement drivers. The framework draws on the prior literature and more recent writings on manufacturing strategy. Section 2 overviews prior research that relates to quality improvement rates._x000B__x000B_Our research design to analyzing quality drivers is triangular — a combination of surveys, data analysis and company interviews. Section 3 reports on perceived drivers of quality from a questionnarie sent to quality engineers and managers in the electronics industry. Section 4 analyzes quality improvement rates and their possible determinants at Solectron, a company that assembles printed circuit boards. Solectron received the Malcolm Baldridge National Quality Award whilst our research was in progress. Additional evidence from interviews with Solectron personnel are provided in Section 5. Section 6 presents implications of the research and possible extensions.