INTRODUCTION: Many educational institutions around the world have adopted e-learning programs. The success of these programs depends on the availability of supporting technologies and the efficiency by which they are utilized. PURPOSE: As was pointed out by Parasuraman et al (1985), differences in perceptions between administrators and stakeholders result in one of the four gaps associated with the design, marketing, and delivery of services. This research is aimed to identify administrators' perceptions of quality in top-ranked e-learning institutions in the U.S. METHOD: The top 100 ranked universities offering E-Learning in the US as of 2018, were identified from U.S. News and World Report. Public announcements made by administrators in these programs were downloaded from the institutions' websites. These were used as units of analysis in performing content analysis utilizing NVivo 12 software (QSR International Pty Ltd. Version 10. 3.2, 2016, Melbourne, Australia). RESULTS: The Pareto chart of the reported frequencies indicated that program administrators tend to stress features, performance, competence, access, communication, understanding, and conformance in their communication to the public. These constructs have a relatively high frequency of occurrence (85%) within the sample of the top 100 universities in the US. Factor analysis (FA) was applied in an attempt to reduce the dimensionality of the data into two uncorrelated factors that can be extracted from the data. These are nontrivial factors accounting for 82% of the total variation. They appear to be used by administrators to distinguish their programs from others within the sample. The results indicate that access, communication, competence, understanding, and responsiveness made significant contributions to the first factor. This factor contributes 69% of the total variability and suggests engagement as a distinguishing factor. Engagement refers to the program's ability to maintain communication, identify students' needs, and address these needs. This is especially important in e-learning where attrition rates are higher than in the face-to-face setting, as was noted by (Allen and Seaman, 2015; Boston and Ice, 2011). The second distinguishing factor includes conformance, reliability, security, and credibility. This factor is shown to contribute 13% of the total variability and can be referred to as trust. In this context, trust relates to the environment and the ability to protect students' information consistently. CONCLUSIONS: While no attempts were made to consider cost nor pedagogical-related factors, this research suggests that administrators consider engagement and trust as decisive factors in achieving quality in e-learning. It is also of interest to identify the perceptions of e-learners and instructors as major stakeholders. These authors are currently designing appropriate instruments that can be used to identify the perceptions of these two important groups. This would help determine perception gaps and direct efforts for quality improvement.