1. An Analysis of Email Response Policies under Different Arrival Patterns By Ashish Gupta Ramesh Sharda An Analysis of Email Response Policies under Different Arrival Patterns By Ashish Gupta Doctoral Student, Department of Management Science & Information Systems, Oklahoma State University, Stillwater. Ramesh Sharda Regents Professor of Management Science & Information Systems, Director, Institute for Research in Information Systems, Oklahoma State University, Stillwater.

2. 01/06/05ICS-20052 Objective of the study To improve individual knowledge worker performance by identifying policies that will :- To improve individual knowledge worker performance by identifying policies that will :- To model email work environment by considering various email characteristics. Improve response time of emails and primary task completion time Reduce number of interruptions Validate the results of prior research.

3. 01/06/05ICS-20053 Problem significance 2004 AMA Research on workplace E-Mail & Productivity 2004 AMA Research on workplace E-Mail & Productivity On a typical workday, time is spent on e-mail is ????? On a typical workday, time is spent on e-mail is ????? 0–59 minutes 77.9% 0–59 minutes 77.9% 90 minutes–2 hours 18% 90 minutes–2 hours 18% 2–3 hours 2% 2–3 hours 2% 3–4 hours 2.5% 3–4 hours 2.5% Osterman Research- How often do you Osterman Research- How often do you check your E-mail for new messages check your E-mail for new messages when at work?

4. 01/06/05ICS-20054 Problem significance E-Policy Institute (2004) E-Policy Institute (2004) Annual Email growth rate= 66 % Annual Email growth rate= 66 % Corporate Research Corporate Research IBM, Microsoft, Xerox, Ferris, Radicati, etc. IBM, Microsoft, Xerox, Ferris, Radicati, etc. Need for more research in MS/IS that Looks at the problem of information overload and interruptions simultaneously. Looks at the problem of information overload and interruptions simultaneously.

5. 01/06/05ICS-20055 Extant Research Overload due to emails- First reported by Peter Denning (1982). Most recently reported by Ron Weber (MISQ, Editor-in-Chief 2004) Interruptions due to emails- Interruptions due to emails- Reported by some- Speier,et.al.1999, Jackson, et.al., 2003, 2002, 2001), Venolia et.al. (2003)

6. 01/06/05ICS-20056 Extant Research “The nature of managerial work”, Mintzberg (1976) “The nature of managerial work”, Mintzberg (1976) “Managerial communication pattern”, Ray Panko (1992) “Managerial communication pattern”, Ray Panko (1992) “Email as a medium of managerial choice”, M. Markus (1994) “Email as a medium of managerial choice”, M. Markus (1994) “You have got (Lots and Lots) of mail” in “The Attention Economy” by Davenport (2001) “You have got (Lots and Lots) of mail” in “The Attention Economy” by Davenport (2001) “The Time Famine: Towards a Sociology of Work Time”, Leslie Perlow (1999) “The Time Famine: Towards a Sociology of Work Time”, Leslie Perlow (1999)

7. 01/06/05ICS-20057 Phenomenon of Interruption Interrupt arrives IL + Interrupt processing Interrupt departs Recall time- RL Pre-processingPost-processing Interruptions- According to distraction theory, interruption is “an externally generated, randomly occurring, discrete event that breaks continuity of cognitive focus on a primary task“ (Corragio, 1990; Tétard F. 2000).

8. 01/06/05ICS-20058 Previous Research Model Performance Measures 1. % Increase in utilization 2. Number of interruptions per task. 3. Primary task completion time 4. Email response time. Task complexity (Pure simple) vs. (more-simple & less-complex) vs. (equal-simple & complex) vs. (less-simple & more-complex) vs. (pure complex) Workload Level Low vs. Medium vs. High Email Policy Flow vs. Scheduled vs. Triage Only “high” dependency on email communication (3 hrs) with exponential email arrivals was studied

9. 01/06/05ICS-20059 Detailed Research model Performance variables (a) % increase in Utilization (b) Time spent due to interruptions (c) Average response time of emails (d) Average completion time of primary task. (e) Total no. of interruptions/ day Email processing strategies (C1, C2, C4, C8, C) Email characteristics Processing Time* (Large, Small) Arrival Rate (V. Low, Low, High, V. High) Dependency on email communication (Very Low, Low, High, Very High) Email arrival pattern (Expo, NSPS) Work Environment * Processing time is based on email category

10. 01/06/05ICS-200510 Email types Emails differentiated on the basis of its ‘content’ or the ‘action required by the user’ Emails differentiated on the basis of its ‘content’ or the ‘action required by the user’ NotationEmail typeDiscrete arrival percentage 1Priority email5% 2Spam5% 3Informative email20% 4Email with non-diminishing service time 55% 5Email with diminishing service time 15%

11. 01/06/05ICS-200511 Email Policies Dependency on Email Communication Policy typeVery Low (1 hr) Low (2 hrs) High (3 hrs) Very High (4 hrs) Notation# of Email hour- slots Triage 8am-9am8am-10am8am-11am8am -12 noon C1 1 Schedule 8am-8:30am 4:30pm- 5pm 8am-9am 4pm-5pm 8am-9:30am 3:30 am to 5:00 pm 8am-10am 3pm- 5pm C2 2 Schedule 8am-8:15am, 11am-11:15am 1pm-1:15pm 4:45pm- 5pm 8am-8:30am, 11am-11;30am 1pm-1:30pm 4:30pm- 5pm 8am-8:45 am, 11am-11:45am, 1 pm - 1:45 pm, 4:15 pm - 5:00 pm 8am-9am 11am - 12 1pm- 2pm 4pm- 5pm C4 4 Schedule 8am-8:08am 9- 9:08am and so on 8-8:15am 9-9:15am 10-10:15am and so on 8-8:23am 9-9:23am 10-10:23am and so on 8- 8:30am 9- 9:30pm 10- 10:30pm and so on C8 8 Flow Processed as soon as emails arrive Processed as soon as emails arrive Processed as soon as emails arrive Processed as soon as emails arrive C Not Applicable

12. 01/06/05ICS-200512 Methodology Discrete event simulation using Arena 8.01 Model Run length= 500 days Model Warm-up time= 50 days No. of replications of each model= 20 16 scenarios evaluated for 5 different policies. Thus, Total number of simulations models= 16 x 5= 80 Total number of data points generated = 80 x 20 = 1600

13. 01/06/05ICS-200513 Scenarios ScenariosEmail (E) dependencyE Arrival patternE processing time 1Very lowTime stationary ExpoSmall 2Very lowTime stationary ExpoLarge 3Very lowNon-Stationary ExpoSmall 4Very lowNon-Stationary ExpoLarge 5LowTime stationary ExpoSmall 6LowTime stationary ExpoLarge 7LowNon-Stationary ExpoSmall 8LowNon-Stationary ExpoLarge 9HighTime stationary ExpoSmall 10HighTime stationary ExpoLarge 11HighNon-Stationary ExpoSmall 12HighNon-Stationary ExpoLarge 13Very HighTime stationary ExpoSmall 14Very HighTime stationary ExpoLarge 15Very HighNon-Stationary ExpoSmall 16Very HighNon-Stationary ExpoLarge

14. 01/06/05ICS-200514 Parameters S # Type 4 email (E) Process ing time (PT) Type 5 E PT (min) Total Email PT per day Avg. Email Arrival Rate Primary Task (P) Arrival Rate /dayE UtilP Util Min (E+P) Util 155112620.1250.7750.9 215 15620.1250.7750.9 355112620.1250.7750.9 415 15620.1250.7750.9 555224520.250.650.9 615 210520.250.650.9 755224520.250.650.9 815 210520.250.650.9 955336420.3750.5250.9 1015 3 420.3750.5250.9 1155336420.3750.5250.9 1215 3 420.3750.5250.9 1355448320.50.40.9 1415 420320.50.40.9 1555448320.50.40.9 1615 420320.50.40.9 Processing time of (a) Type 1 email- Expo(10 min) (b) Type 2 email- Expo (0.5 min) (c) Type 3 email- Expo (5 min) (d) Primary task- Expo(6 min)

15. 01/06/05ICS-200515 Bird’s Eye view of Entire model built using Arena Zoom in follows….

16. Arena Email flow Snapshot 1 2 1 2 Preempts the KW when an email of type 1 arrives during email hrs. Stores remaining processing time in an attribute ‘RT’ 3 3 Releases emails of type 2,3,4 on the basis of policy Emails created based on different schedules that determines whether it is Expo or Non-Stationary Expo and at what rate To record output statistics of each email type separately Checks if email has been in system for > or < than 24 hrs

17. 01/06/05ICS-200517 Arena Primary Task Snapshot Attribute RT is reset to 0 to erase the memory. This makes the attribute RT reusable for recording remaining time interrupted primary task in future. Checks to see if RT>0. If yes, RL and IL are added If no, Primary task is sent next processing stage

19. 01/06/05ICS-200519 Model Logic New email arrival Ei occurs at time T0, for all i ={n : n = 1.. 5} If i = 1, Step1. Email released at T0. Step2. If STATE (KW) == IDLE & E1.WIP=0 KW seized; Than, Set RT = Ta = 0; IL = 0, RL = 0; Process E1; Release KW; If STATE (KW) == BUSY & E1.WIP=0 Seize KW; Than, Set RT = Ta; Record IL = Tria (a, b, c), Tb; Process E1; Release KW; Calculate; χ = Tb /( Ta + Tb) for all0 ≤ χ ≤ 1 Calculate; RL = {RT * [χ * *( K-1)] * [ (1- χ)* * ( L-1 ) } / Beta (K,L)

20. 01/06/05ICS-200520 Model Logic For K = 2, L = 1; For K = 2, L = 1; Calculate; Calculate; T1 = IL + Tb + RL; Seize KW for time T1; Process Pi Set RT=0; Release KW; If i = 2 || 3 || 4 || 5, Step.3 Release Ei, if {(STATE(dummy) == IDLE_RES && Process email 1234.WIP == 0 && email 5 in 1.WIP == 0 && email 5 in 2.WIP == 0 ) || ( STATE(anti dummy) == IDLE_RES && Primary.WIP == 0 && NQ(Hold primary.Queue) == 0 && IL Primary.WIP=0 && RL primary.WIP == 0 ) } = TRUE Else Hold;

21. 01/06/05ICS-200521 Model logic- comments If New arrival = Pn Step4. Release if, STATE(kW) == IDLE_RES; Else Hold; //***** Tb- Value added time spent on the task Before interruption Ta- Value added time spent on the task After interruption χ - Fraction of task completed before interruption occurred IL – Interruption Lag RL – Resumption Lag Pi – interrupted primary task Dummy resource- implements email hours Anti-dummy resource – implements non- email hours *****// *****// Stop;

22. Results (a) Percent Increase in Utilization

23. Results (b) Additional Time (min) spent per day due to interruptions

24. 01/06/05ICS-200524 Response time results Avg. Email Response Time = Avg. Email processing time (Value added) + Avg. Email wait (Queue) time [fig. c] Avg. Primary Task (PT) Completion Time [fig. d.3] = Avg. PT value added processing time + Avg. PT non-value added processing time due recalling & switching [fig. d.1] + Avg. PT wait (Queue) time [fig. d.2]

25. Results (c) Email Wait time i.e. inbox queue and holdup time

26. Results (d.1) Avg. Additional time spent (wasted) in recalling and switching for processing one primary task

27. Results (d.2) Average Primary Task Wait Time

28. Results (d.3) Average Primary Task Completion Time

29. 01/06/05ICS-200529 Optimal Policy ?? Previous research found C4 as the optimal policy (no consideration was given to email arrival pattern and characteristics). Previous research found C4 as the optimal policy (no consideration was given to email arrival pattern and characteristics). Current Research found under varying email arrival characteristics- Current Research found under varying email arrival characteristics- Optimal policy for primary task completion time - C1 & C2 closely followed by C4. Optimal policy for primary task completion time - C1 & C2 closely followed by C4. Optimal policy for email response time – C Optimal policy for email response time – C Optimal policy for reducing interruptions- C1& C4 closely followed by C2 Optimal policy for reducing interruptions- C1& C4 closely followed by C2

30. 01/06/05ICS-200530 Limitations of the model Assumptions of the model are its limitations Assumptions of the model are its limitations Knowledge worker works strictly according from 8 to 12 and then from 1 to 5pm. Need for relaxing the work-hrs. Knowledge worker works strictly according from 8 to 12 and then from 1 to 5pm. Need for relaxing the work-hrs. Knowledge worker is busy only 90% of the time in a given workday. Knowledge worker is busy only 90% of the time in a given workday. KW is working on interruptible primary task. In reality, not all primary tasks are interruptible. For e.g. group meetings KW is working on interruptible primary task. In reality, not all primary tasks are interruptible. For e.g. group meetings Primary task modeled is interruptible only 3 times. Primary task modeled is interruptible only 3 times. Emails are not interrupted. Emails are not interrupted.

31. 01/06/05ICS-200531 Limitations & future research Perform the study in field or experimental settings. Perform the study in field or experimental settings. Modeling utility/ life of an email. Modeling utility/ life of an email. Modeling group knowledge network and at organizational level. Modeling group knowledge network and at organizational level. Modeling by incorporating more doses of reality. Considering other communication media along with email. Modeling by incorporating more doses of reality. Considering other communication media along with email. http://iris.okstate.edu/rems/ Suggestions or comments or Questions????