PurposeThis study aims to propose and validate a model for e-Learning success based on students’ experiences in the “new normal.” To achieve this goal, this study focused on answering three research questions: (1) What are the students’ experiential factors that impact e-Learning? (2) How do these experiential factors affect e-Learning success? (3) In what ways does a multimethod provide a comprehensive perspective and an in-depth understanding of students’ e-Learning experiences in the new normal?Design/methodology/approachThis study applied a mixed-methods sequential approach using exploratory, confirmatory and complementary studies. First, this study undertook a text-mining exploratory analysis of the review data to extract e-Learning topics. Then, based on the Information Systems (IS) success model, this study identified an integrated framework drawn from the results of the text-mining analysis. Second, this study proposed an e-Learning, experience-based success model and corresponding hypotheses and conducted a confirmatory study with surveys to validate the model. Third, this study conducted in-depth interviews to better identify the phenomenon of interest.FindingsThe five factors extracted from the first stage are system quality, lecture content, teaching quality, online interaction and achievement. This study subsequently confirmed the significant relationships between the e-Learning success factors in the second stage based on the IS success model. Finally, a complementary study identified the importance of interactivity for e-Learning success in the new normal.Originality/valueTo the best of the authors’ knowledge, this paper is the first to develop an e-Learning success model using a comprehensive mixed-methods approach.

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Skippable ads are known to provide better advertising experiences by giving viewers a sense of control with the ability to skip an ad after watching it for a few seconds. Despite the growing interest, few studies have investigated factors that influence skipping or viewership of pre-roll skippable ads. This study examines the determinants of pre-roll ad acceptance by using clickstream data of 2,078,090 users' ad and content viewing behaviors on a popular online video content platform in South Korea. We found that skippable ad acceptance is impacted by personal, situational, and contextual factors that influence the level of goal impediment in online video consumption contexts. We conclude with theoretical and practical implications of the findings.

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With the recent spread of digital content, patterns of media viewing have changed. This is especially true for programs formerly watched on TV but are now increasingly viewed through online videos. As more and more people watch online videos, the market for online video advertising is increasing. Including online video advertising, online advertising can be effective if advertisers and online service providers attract as many viewers as possible. In particular, service providers try to maximize their profits by efficiently selling advertising inventory, which indicates the volume of space available for advertisements. However, most of today's service providers use simple statistical applications to predict advertising inventory that leads to relatively inaccurate predictions. Therefore, this study aims to develop a model capable of accurately predicting advertising inventory and then validate the model. This study in predicting online video advertising inventory is based on using deep learning to analyze the raw data of online video channels and then comparing the results of these predictions with actual inventory, other results of machine learning techniques, and work-site method results. Using these techniques and approaches, future advertising inventory can be more accurately predicted. In addition, detailed strategies for the practice of online video advertising are suggested. INDEX TERMS advertising inventory, online video advertising, TV programs, deep learning, predictionThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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