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This paper examines the effects of providing broadband to schools on students' performance. We use a rich panel of data on broadband use and students' grades from all middle schools in Portugal. Employing a first-differences specification to control for school-specific unobserved effects and instrumenting the quality of broadband to account for unobserved time-varying effects, we show that high levels of broadband use in schools were detrimental for grades on the ninth-grade national exams in Portugal. For the average broadband use in schools, grades reduced 0.78 of a standard deviation from 2005 to 2009. We also show that broadband has a negative impact on exam scores regardless of gender, subject, or school quality and that the way schools allow students to use the Internet affects their performance. In particular, students in schools that block access to websites such as YouTube perform relatively better. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1770 . This paper was accepted by Lorin Hitt, information systems.

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Video is one of the fastest growing online services offered to consumers. The rapid growth of online video consumption brings new opportunities for marketing executives and researchers to analyze consumer behavior. However, video introduces new challenges. Specifically, analyzing unstructured video data presents formidable methodological challenges that limit the current use of multimedia data to generate marketing insights. To address this challenge, the authors propose a novel video feature framework based on machine learning and computer vision techniques, which helps marketers predict and understand the consumption of online video from a content-based perspective. The authors apply this frame-work to two unique datasets: one provided by Masterclass.com, consisting of 771 online videos and more than 2.6 million viewing records from 225,580 consumers, and another from Crash Course, consisting of 1,127 videos focusing on more traditional education disciplines. The analyses show that the framework proposed in this paper can be used to accurately predict both individual-level consumer behavior and aggregate video popularity in these two very different contexts. The authors discuss how their findings and methods can be used to advance management and marketing research with unstructured video data in other contexts such as video marketing and entertainment analytics.

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