Learning by Doing versus Learning by Viewing: An Empirical Study of Data Analyst Productivity on a Collaborative Platform at eBay

We investigate how data-analyst productivity benefits from collaborative platforms that facilitate learning-by-doing (i.e. analysts learning by writing queries on their own) and learning-by-viewing (i.e. analysts learning by viewing queries written by peers). Learning is measured using a behavioral (productivity-improvement) approach. Productivity is measured using the time from creating an empty query to first executing it. Using a sample of 2,001 data analysts at eBay Inc. who have written 79,797 queries from 2014 to 2018, we find that: 1) learning-by-doing is associated with significant productivity improvement when the analyst's prior experience focuses on the focally queried database; 2) only learning-by-viewing queries that are authored by analysts with high output rate (average number of queries written per month) is associated with significant improvement in the viewer's productivity; 3) learning-by-viewing also depends on the ''social influence'' of the author of the viewed query, which we measure 'locally' based on the number of the author's direct viewers per month or 'globally' based on the how the author's queries propagate to her peers in the overall collaboration network. Combining results 2 and 3, when segmenting analysts based on output rate and 'local' social influence, the viewing of queries authored by analysts with high output but low local influence is associated with the largest improvement in the viewer's productivity; whereas when segmenting based on output rate and 'global' social influence, the viewing of queries authored analysts with high output and high global influence is associated with the largest improvement in the viewer's productivity. Overall, regardless of the segmentation, learning-by-viewing is associated with greater productivity improvement than learning-by-doing in our study.