Using Data from Digital
Traces
Bradley R. Staats
Visiting Associate Professor, The Wharton School
University of Pennsylvania
Associate Professor, UNC Kenan-Flagler
Trace data: A sign or evidence of some
past thing
http://hadoopilluminated.com/hadoop_illuminated/Big_Data.html
1. Introduction
2. Examples
3. Issues to Consider
1. Digital trace: Process data
Application
received &
scanned
Custodian
Yes
Document
Tagging
Application
Capture (1 &
2)
Yes
Preliminary
Information (1 & 2)
Policy fail?
No
No
Automatic
incomplete
application letter
No
111 workers over 2 ½ years
598,393 individual transactions
Additional
Application
Capture (1 &
2)
Document
Tagging
Yes
Materials
received &
scanned
Custodian
Application
Complete?
Yes
Credit Check
(1 & 2)
Automatic request
for additional
materials
No
Yes
Policy fail?
No
Application
Complete?
No
Automatic
incomplete
application letter
Marginal
Yes
Policy fail?
No
Automatic
rejection letter
sent
Income Tax
(1 & 2)
Policy fail?
No
Real Estate
(1 & 2)
Routed to
credit expert
for negotiation
Credit
Approval
Yes
Yes
No
Yes
1. Introduction
2. Examples
Automatic
approval letter
sent
3. Issues to Consider
Automatic
Automatic
rejection letter
rejection letter
sent
sent
A perennial problem in industry has been that of sustaining
human productivity over extended periods of time.
–Scott 1966: 4
Specialization
(Smith 1776; Taylor
1911; Skinner 1974;
Boh et al. 2007; Schultz
et al. 2003)
Variety
Task
Allocation
Strategy?
(Hackman & Oldham
1976; Schilling et al.
2003; Narayanan et al.
2009)
Findings
• During a day: Specialization > Varied
assignment
• Across days: Varied assignment >
Specialization
• Workers exhibit learning in setups
Staats & Gino (2012). Specialization and variety in
repetitive tasks: Management Science.
2. Digital trace: Click data
1. Introduction
2. Examples
3. Issues to Consider
2. Digital trace: Click data
How does the team encourage (or discourage)
individual knowledge sourcing behavior?
• Knowledge repository data
– Per-click data by person
• Software development project data
– Project outcomes, characteristics
– 487 projects
• HR system data
– E.g., demographics
1. Introduction
2. Examples
3. Issues to Consider
3. Digital trace: Search data
• Do temporal landmarks motivate aspirational
behavior
Google searches for “diet” (average)
75
75
70
65
60
55
50
Mon Tue Wed Thu Fri
Day of the Week
First workday after
federal holidays
70
65
60
55
50
Sat
Sun
-4
-3
-2 -1
0
1
2
3
Days Since the First Workday
After a Federal Holiday
4
Dai, Milkman & Riis (Forthcoming). The Fresh Start
Effect. Management Science.
1. Introduction
2. Examples
3. Issues to Consider
4. Digital trace: Tracking data
RFID tags monitoring hand
washing compliance
Personalized messaging
Data transfer to a central server
13,773,068 hand hygiene
opportunities
• Generated by 4,157 caregivers at 35 hospitals
from January 2010 to March 2013
Dai, Hengchen, Milkman, Katherine L., Hofmann, David A., & Staats, Bradley R. The Impact of Time at Work
and Time off from Work on Rule Compliance: The Case of Hand Hygiene in Healthcare.
1. Introduction
2. Examples
3. Issues to Consider
4. Digital trace: Tracking data
How do job demands affect individual compliance over the course of a single shift?
Dai, Hengchen, Milkman, Katherine L., Hofmann, David A., & Staats, Bradley R. The Impact of Time at Work
and Time off from Work on Rule Compliance: The Case of Hand Hygiene in Healthcare.
1. Introduction
2. Examples
3. Issues to Consider
Issues to Consider
• Access
– Relationship building
– Pipeline management
• Structure to the data
• Mechanisms
– Mixing other methods
– Or maybe leveraging
more trace data…
• Informed consent
1. Introduction
2. Examples
Myers, Chris, Staats, Bradley R., & Gino, Francesca. “My Bad”: The
Impact of Internal Attribution and Ambiguity of Responsibility on
Learning from Failure.
3. Issues to Consider