Modeling & Simulation of Knowledge Worker Attention for Evaluation of Email Processing Strategies

1. Modeling & Simulation of Knowledge Worker Attention for Evaluation of Email Processing Strategies Robert A. Greve

2. Agenda • Introduction • Literature Review • Research Questions, Propositions, and Hypotheses • Case Study Results • Model Development • The Simulation Model • Results • Summary and Conclusions

3. Introduction • “… we have little published research about e-mail …” (Ron Weber, Editorial Comments, MISQ, September 2004) • 68% check email “more or less continuously” and 17% check email “a few times per hour.” (Osterman Research, D’Antoni, 2004) • Email Reactions - 70% within 6 seconds, 85% within 2 minutes; time to recover from email interrupts: 64 seconds (Jackson, 2003)

4. Literature • Interruptions • Interrupted tasks require more time than uninterrupted tasks (Czerwinski, et al., 2000a) • Effects are moderated by: • task’s complexity (Speier, 1999) • the interrupt’s similarity (Speier, 1999; Czerwinski, et al., 2000b), • the phase of the task (Cutrell, et al., 2000; Czerwinski, et al., 2000b; Monk, et al. 2002)

5. Literature • Interruptions • Interruptions are preceded by an Interruption Lag and followed by a Resumption Lag (Trafton, 2003) Project Work → Email Alert → Interruption Lag → Email Processing → Resumption Lag → Project Work • On average it takes a worker 64 seconds to recover from an email interruption. The average time taken to react to an arriving email was only 1 minute 44 seconds. Over 70% reacted to the email within six seconds of the email arrival (Jackson, 2003).

6. Literature • Asynchronous Communication • Asynchronous meetings require more time (Hightower and Sayeed, 1996) • “Groups working in the asynchronous mode of communication will report that the group’s problem solving process is less efficient than will groups working in the face-to-face mode of communication (Dufner, et al., 2002)

7. Literature • Email Overload Solutions • Filtering (SPAM filters…, Sharda, et al., 1999) • Organizing (Marson, 2000; Rennie, 2000; Balter, 2000) • Prioritizing (Balter, 2000; Losee, 1989; Horvitz, Jacobs, and Hovel, 1999) • Timing (Jackson, 2003)

8. Experiments (Testing of Hypotheses) Email Environment Performance -Efficiency -Hours Worked -Email Resolution Time Email Processing Strategy

9. Research Objectives • Answer questions concerning the impact of a knowledge worker’s email processing strategy on the knowledge worker’s performance • Prescribe email processing strategy solutions for given environments and objectives

10. REMS (Research in Email Management Strategies) • Gupta, A., Sharda, R., Greve, R., Kamath, M., & Chinnaswamy (2005) How often should we process email? Balancing interruptions and quick response times, to be presented at The 2005 Big 12 IS Research Symposium • Key Differences • Based on information and data collected from knowledge workers involved in long term projects, instead of tasks • Capturing total hours worked and efficiency, instead of utilization • Modeling of the knowledge worker’s attention as an entity, separate from the email entities • Separation of urgent from non-urgent email • Use of optimization tool

12. Research Questions • Research Question 1: Where is the middle ground? How many controlled interruptions is enough to allow for appropriate resolution times?

13. Research Questions • Proposition 1(a): Dividing non-priority email work into two specific time frames (holding email hours twice daily) will allow for successfully replying to all email within the 24 hour appropriate time frame. • Proposition 1(b): Processing email in batches corresponding to 1/2 of an average daily email processing load will allow for successfully resolving all email within the 24 hour appropriate time frame.

14. Research Questions • Research question 2: To what extent will fewer interruptions result in more efficient work completion? • Hypothesis 2(a): Dividing non-priority email work into two specific time frames will result in significantly greater efficiency when compared to processing email continuously. (Speier, 1999 & 2003; Trafton, 2003; and Jackson (2003) • Hypothesis 2(b): Processing email in batches corresponding to 1/2 of an average daily email processing load will result in significantly greater efficiency when compared to processing email continuously. (Speier, 1999 & 2003; Trafton, 2003; and Jackson (2003)

15. Research Questions • Research question 3: To what extent will fewer interruptions lower information overload, as indicated by the numbers of hours worked daily? • Hypothesis 3(a): Holding email hours twice daily will result in significantly fewer total hours worked daily when compared to processing email continuously. (Speier, 1999 & 2003; Trafton, 2003; and Jackson (2003) • Hypothesis 3(b): Processing email in batches corresponding to 1/2 of an average daily email processing load will result in significantly fewer total hours worked daily when compared to processing email continuously. (Speier, 1999 & 2003; Trafton, 2003; and Jackson, 2003)

16. Research Questions • Research Question 4: To what extent will email arrival patterns influence the success of given email processing strategies? • Proposition 4(a): Email hours scheduled during peaks in arrival patterns will have significantly shorter resolution times when compared to email hours not scheduled during peaks in arrival patters. • Proposition 4(b): Email processed in batches will have significantly shorter resolution times when compared to email hours not scheduled during peaks in arrival patters. • Proposition 4(c): Email processed in batches will not have significantly different resolution times when compared to email hours scheduled during peaks in arrival patterns.

17. Research Questions • Research Question 5: Can an optimization tool be used in conjunction with simulation to automate the analysis of email processing strategies in finding an optimal email processing strategy for specific performance objects and constraints? • Proposition 5: Optquest, coupled with the Arena simulation tool will produce results consistent with those obtained through analysis of the Arena simulation’s output.

18. Research Approach • Case Study Assessment • Modeling of the Knowledge Work Environment • Simulation (ARENA) • Experiments (Testing of Propositions & Hypotheses) • Optimization through Simulation (Optquest)

19. Case Study Results • Project managers interviewed • Email is an essential tool • Email is monitored continuously • Email is intrusive • Email overload is a real problem • Processing all for the few • Never caught up • Go home when a milestone is reached

20. A Typical Day of Email

21. Mathematical Model

22. Mathematical Model

23. Mathematical Model Wqjs email’s wait in the queue (time spent waiting for the knowledge worker’s attention) for email of urgency j, having sequence number s Wsjs email’s wait in the system (email resolution time) for email of urgency j having sequence number s Wsjs = Wqjs + Pkds __ Wsj mean email resolution time for email of of urgency j __ Wsj= ∑sWsjs / S

24. Mathematical Model Ydtotal email processing occurring on day d Yd = ∑k∑s Pkds Zdtotal amount of primary work completed on day d Zd >= Qd - Yd Gdtotal lag time occurring on day d Gd = ∑s Lds + ∑s Rds

25. Mathematical Model Hdtotal hours worked by the knowledge worker on day d Hd = Yd+ Zd + Gd __ H mean hours worked by the knowledge worker ∑dHd / D Edknowledge worker efficiency occurring on day d Ed= (Yd+ Zd)/ Hd __ E mean knowledge worker efficiency ∑dEd / D

26. Arena Simulation

27. Email Flow, Statistics, & Disposal Submodel

28. The Continuous Email Processing Strategy Submodel

29. Non-Continuous Email Processing Submodels

30. Simulation Implementation • Warm Up – 30 days • Run Length – 90 days • Replications – 40 • Types of Simulations – 13 • Observations (n) – 520

31. Results MANOVA Model Results __ __ __ __ __ __ H + E + Ws1 + Ws2 + Ws3 + Ws4 = X The email processing strategy (X) had a significant main effect. A statistically significant difference was found between groups (α = 0.001).

33. Results of All Strategies

34. Results of All Strategies

35. Results of All Strategies

36. Optquest • Find policy X* from set of policies X, such that objective function Z is optimized, subject to constraint C. Example: _ Max E __ S.T. Ws2 <= 3

37. Optquest Implementation

38. Results • Proposition 5: Optquest, coupled with the Arena simulation tool will produce results consistent with those obtained through analysis of the Arena simulation’s output. • SUPPORTED (Results were consistent with those obtained through brute force search)

39. Results of OptQuest

40. Contributions to Knowledge • One more piece of the puzzle (incremental contribution from Gupta, et al. (2005)) • Overlooked solution • Timing of email processing • Separation of urgent from non-urgent email • Consideration of high-level knowledge workers • New understanding of knowledge workers’ email challenges • Processing strategies had little to do with efficiency. • Unique analytical approaches • Simulation of Knowledge Worker Attention • Optquest Optimization through Simulation

41. Contributions to Knowledge • Demonstration of 3% gain in efficiency without sacrificing email processing success • Demonstration of need for policies specifying urgency of email in need of processing

42. Future Research • Expanded modeling of email environments • Expanded modeling of email processing strategies • The use of Optquest in creation of an “ESDSS” • Testing in “real” environment

43. Questions?