Study Hacks Blog Decoding Patterns of Success Posts from 2018 October

The Average User Checks Email 5.6 Hours Per Weekday. This Is Not Good.

October 9th, 2018 · 20 comments

A Stark Survey

A couple months ago, Adobe released the results of its fourth annual Consumer Email Survey. Drawing from data gathered earlier in the summer from over 1000 panel participants, the survey provides a snapshot of current consumer email habits.

Among other results, it reveals that self-reported time spent checking work email has decreased slightly to 3.1 hours per weekday on average. By contrast, the average time spent checking personal email has increased by almost 20% to 2.5 hours per weekday.

Combined: the average daily time spent checking email is now 5.6 hours — up almost a half hour since 2017.

These numbers are self-reported and therefore should not be taken too literally, and if you look at the histogram provided by Adobe, it’s clear that the variance is significant. The survey still captures, however, the stark reality that the average professional is now dedicating a substantial fraction of their waking hours to sending and receiving digital messages.

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On the Law of Diminishing Specialization

October 3rd, 2018 · 19 comments

On Productive Technology and its Discontents

Recently, I’ve been dipping in and out of Edward Tenner’s provocative 1996 book, When Things Bites Back. In following one of Tenner’s footnotes I came across a fascinating 1992 academic study from the National Review of Productivity, authored by the Georgia Tech economist Peter G. Sassone

The paper has an innocuous title, “Survey Finds Low Office Productivity Linked to Staffing Imbalances,” but its findings are profoundly relevant to our recent discussion of attention capital theory, and the value of deep work more generally.

Beginning in 1985, Sassone began a series of twenty office productivity case studies spread over different departments in five major U.S. corporations. His initial goal was to measure the bottomline benefits of the front office computer systems that were new at the time, but as he notes, this soon changed:

“[I]t became apparent that [my] data collection and analysis techniques were yielding important productivity insights beyond the cost justification of office computer systems.”

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