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Short description of portfolio item number 1
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Published in , 2021
Entertainment television today is being increasingly consumed via online video on demand (VOD) services. A VOD service is less constrained compared to the traditional (linear) TV in terms of the number of programs it can simultaneously offer, allowing its viewers to watch a program at a time of their choice. On the one hand, offering more programs can dilute the quality of the programs; while on the other, the time flexibility given to the viewers creates an incentive for the VOD service to produce high-quality programs. In this paper, we theoretically study how the ‘on-demand’ feature affects the number of programs and the investment in their qualities of a monopolist when viewers are heterogeneous in their keenness to watch television programs, their preferred genre (taste preference) and, importantly, their preferred time to watch the programs (time preference). We first show that, when offering the same number of programs, while the linear TV service always offers them at the same quality, the VOD service can offer them at different qualities. When the VOD service offers more programs—utilizing its capacity advantage to better cater to viewer tastes—the offering can comprise both higher and lower quality programs compared to the linear TV. We also find that, while VOD increases consumer welfare, it is not always Pareto-improving because some viewers may only consume a lower quality program. All the results are driven by the heterogeneity in viewer time preferences, and are in line with the real world observations about the content of popular VOD platforms.
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Published in , 2022
In companies today, work is organized in fluid teams whose composition changes as certain (new) skills become important while others become less relevant. Employees in-turn are primarily responsible for navigating their careers through positions that match their expertise and ambition. As retention and advancement of good employees is vital, many organizations provide employees the opportunity to apply for new jobs listed in the organization. Facilitating and supporting this internal mobility is a novel matching problem faced by many firms. We partnered with the IT division of a large financial services firm to develop an algorithmic model of an (internal) applicant’s probability of selection to a new position. We show that the informational content in the applicant’s current job’s description and the firm’s past selection decisions contain valuable signal to predict selection of internal applicants. The model enables the firm to identify cases where the applying employee’s chances of selection was predictably low. This allows the firm to design training programs that are tailored to improve employees’ selection prospects to their observed choices among new positions. We verify that these training opportunities are consistent with the firm’s incentives and summarize them to an interpretable set via clustering. Our machine learning approach integrates three different sources of information available to firms, to identify training opportunities directly linked to new positions and observed employee choices.
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A presentation at the annual AI-Strategy consortium of the ISB based on this paper.
Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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