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Following trends in entertainment streaming services, online educational platforms are increasingly offering users flexible "on-demand" content options. It is important to understand how the timing of content release affects learning behaviors and firm revenue drivers. The current research studies over 67,000 users taking a marketing course before versus after a natural experiment in which the platform switched the course from a scheduled weekly-release format to an on-demand format with all content immediately available. The switch to on-demand positively impacted short-term firm revenue by increasing the number and proportion of certificate-paying users, suggesting that on-demand content can attract a broader set of consumers who value flexibility. On the downside, the switch resulted in users exhibiting lower lecture completion rates and quiz performance and taking fewer additional business courses on the platform, representing a long-term cost. The results were robust to propensity score matching and stratification. The analyses also revealed that on-demand content enabled learning patterns that deviated from a standard evenly paced schedule, including "strategic" binge learning and stretching out engagement past the recommended course period. Thus, while on-demand formats can boost revenues by bringing in more paying users, managers must consider new strategies for maintaining performance and engagement levels within these environments. [ABSTRACT FROM AUTHOR]

Null hypothesis significance testing (NHST) is the default approach to statistical analysis and reporting in marketing and the biomedical and social sciences more broadly. Despite its default role, NHST has long been criticized by both statisticians and applied researchers, including those within marketing. Therefore, the authors propose a major transition in statistical analysis and reporting. Specifically, they propose moving beyond binary: abandoning NHST as the default approach to statistical analysis and reporting. To facilitate this, they briefly review some of the principal problems associated with NHST. They next discuss some principles that they believe should underlie statistical analysis and reporting. They then use these principles to motivate some guidelines for statistical analysis and reporting. They next provide some examples that illustrate statistical analysis and reporting that adheres to their principles and guidelines. They conclude with a brief discussion. [ABSTRACT FROM AUTHOR]

SCORIGHT is a very general computer program for scoring tests. It models tests that are made up of dichotomously or polytomously rated items or any kind of combination of the two through the use of a generalized item response theory (IRT) formulation. The items can be presented independently or grouped into clumps of allied items (testlets) or in any combination of the two. When there are testlets, the program assesses the degree of local dependence and adjusts the estimates accordingly. The estimation is accomplished within a fully Bayesian framework using Markov chain Monte Carlo procedures, which allows the easy calculation of many important characteristics of the scores that are not available with other methods. The current version of SCORIGHT, version 3.0, includes a new module that allows the user to include covariates in the analysis.

Online educational platforms increasingly allow learners to consume content at their own pace with on-demand formats, in contrast to the synchronous content of traditional education. Thus, it is important to understand and model learner engagement within these environments. Using data from four business courses hosted on Coursera, the authors model learner behavior as a two-stage decision process, with the first stage determining across-day continuation (vs. quitting) and the second stage determining within-day choices among lectures, quizzes, and breaks. By modeling the heterogeneity across learners pursuing lecture and quiz completion goals, the authors capture different patterns of consumption that correspond to extant theories of goal progress within an empirical field setting. They find that most individuals exhibit a learning style whereby lecture utility changes as an inverted U-shaped function of current progress. This model may also be used as an early detection system to anticipate changes in engagement, and it enables the authors to relate learning styles to final performance outcomes and enrollment in additional courses. Finally, the authors examine how quiz-taking varies across learners in different courses and between those who have paid versus not paid for the option to earn a course certificate. [ABSTRACT FROM AUTHOR]