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Using Data from Digital Traces

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.

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Using Data from Digital Traces

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  1. Using Data from Digital Traces Bradley R. StaatsVisiting Associate Professor, The Wharton SchoolUniversity of Pennsylvania Associate Professor, UNC Kenan-Flagler

  2. 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

  3. 1. Digital trace: Process data Materials received & scanned Document Tagging Additional Application Capture (1 & 2) Preliminary Information (1 & 2) Application received & scanned Document Tagging Application Capture (1 & 2) Automatic rejection letter sent Yes Yes Custodian Policy fail? No No Automatic incomplete application letter No Application Complete? 111 workers over 2 ½ years 598,393 individual transactions Yes Credit Check (1 & 2) Automatic rejection letter sent Automatic request for additional materials Automatic rejection letter sent No Yes Yes Custodian Policy fail? No Automatic incomplete application letter No Application Complete? Routed to credit expert for negotiation Marginal Yes Income Tax (1 & 2) Real Estate (1 & 2) No No Credit Approval Policy fail? Policy fail? Yes Automatic approval letter sent No Yes Yes 1. Introduction 2. Examples 3. Issues to Consider

  4. 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 (Hackman & Oldham 1976; Schilling et al. 2003; Narayanan et al. 2009) Task Allocation Strategy? • 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.

  5. 2. Digital trace: Click data 1. Introduction 2. Examples 3. Issues to Consider

  6. 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

  7. 3. Digital trace: Search data • Do temporal landmarks motivate aspirational behavior Google searches for “diet” (average) First workday after federal holidays Dai, Milkman & Riis (Forthcoming). The Fresh Start Effect. Management Science. 1. Introduction 2. Examples 3. Issues to Consider

  8. 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

  9. 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

  10. Issues to Consider • Access • Relationship building • Pipeline management • Structure to the data • Mechanisms • Mixing other methods • Or maybe leveraging more trace data… • Informed consent Myers, Chris, Staats, Bradley R., & Gino, Francesca. “My Bad”: The Impact of Internal Attribution and Ambiguity of Responsibility on Learning from Failure. 1. Introduction 2. Examples 3. Issues to Consider

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