1 / 31

Objective of the study

iden
Download Presentation

Objective of the study

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. An Analysis of Email Response Policies under Different Arrival PatternsBy Ashish GuptaDoctoral 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. Objective of the study • 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. ICS-2005

  3. Problem significance • 2004 AMA Research on workplace E-Mail & Productivity • On a typical workday, time is spent on e-mail is ????? • 0–59 minutes 77.9% • 90 minutes–2 hours 18% • 2–3 hours 2% • 3–4 hours 2.5% • Osterman Research- How often do you check your E-mail for new messages when at work? ICS-2005

  4. Problem significance • E-Policy Institute (2004) • Annual Email growth rate= 66 % • Corporate Research • IBM, Microsoft, Xerox, Ferris, Radicati, etc. • Need for more research in MS/IS that • Looks at the problem of information overload and interruptions simultaneously. ICS-2005

  5. Extant Research • Overload due to emails- • First reportedby Peter Denning (1982). • Most recently reported by Ron Weber (MISQ, Editor-in-Chief 2004) • Interruptions due to emails- • Reported by some- Speier,et.al.1999, Jackson, et.al., 2003, 2002, 2001), Venolia et.al. (2003) ICS-2005

  6. Extant Research • “The nature of managerial work”, Mintzberg (1976) • “Managerial communication pattern”, Ray Panko (1992) • “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) • “The Time Famine: Towards a Sociology of Work Time”, Leslie Perlow (1999) ICS-2005

  7. Recall time- RL IL + Interrupt processing Pre-processing Post-processing Interrupt arrives Interrupt departs Phenomenon of Interruption 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). ICS-2005

  8. Task complexity • (Pure simple) vs. (more-simple & less-complex) vs. (equal-simple & complex) vs. (less-simple & more-complex) vs. (pure complex) Email Policy Flow vs. Scheduled vs. Triage Performance Measures 1. % Increase in utilization 2. Number of interruptions per task. 3. Primary task completion time 4. Email response time. Workload Level Low vs. Medium vs. High Previous Research Model Only “high” dependency on email communication (3 hrs) with exponential email arrivals was studied ICS-2005

  9. Email processing strategies (C1, C2, C4, C8, C) Work Environment 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 Dependency on email communication (Very Low, Low, High, Very High) Email arrival pattern (Expo, NSPS) Email characteristics Processing Time* (Large, Small) Arrival Rate (V. Low, Low, High, V. High) Detailed Research model * Processing time is based on email category ICS-2005

  10. Email types • Emails differentiated on the basis of its ‘content’ or the ‘action required by the user’ ICS-2005

  11. Email Policies ICS-2005

  12. 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 ICS-2005

  13. Scenarios ICS-2005

  14. Parameters 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) ICS-2005

  15. Bird’s Eye view of Entire model built using Arena Zoom in follows…. ICS-2005

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

  17. Arena Primary Task Snapshot Checks to see if RT>0. If yes, RL and IL are added If no, Primary task is sent next processing stage 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. ICS-2005

  18. 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 all 0 ≤ χ ≤ 1 Calculate; RL = {RT * [χ * *( K-1)] * [ (1- χ)* * ( L-1 ) } / Beta (K,L) ICS-2005

  19. Model Logic For K = 2, L = 1; 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; ICS-2005

  20. 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; Stop; Stop; ICS-2005

  21. Results (a) Percent Increase in Utilization

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

  23. 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] ICS-2005

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

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

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

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

  28. Optimal Policy ?? • 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- • Optimal policy for primary task completion time - C1 & C2 closely followed by C4. • Optimal policy for email response time – C • Optimal policy for reducing interruptions- C1& C4 closely followed by C2 ICS-2005

  29. Limitations of the model • 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 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 • Primary task modeled is interruptible only 3 times. • Emails are not interrupted. ICS-2005

  30. Limitations & future research • Perform the study in field or experimental settings. • Modeling utility/ life of an email. • Modeling group knowledge network and at organizational level. • Modeling by incorporating more doses of reality. Considering other communication media along with email. http://iris.okstate.edu/rems/ Suggestions or comments or Questions???? ICS-2005

More Related