1. By Ashish Gupta Ramesh Sharda Robert Greve Manjunath Kamath Mohanraj Chinnaswamy How often should we check our email? Balancing interruptions and quick response times By Ashish Gupta Ramesh Sharda Robert Greve Manjunath Kamath Mohanraj Chinnaswamy

2. 03/02/052005 Big XII IS Symposium- OU2 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 :- By improving email response time & primary task completion time. Reduce number of interruptions. Validate the results of prior research. Validate the results of prior research. To model email work environment by considering various email characteristics To model email work environment by considering various email characteristics

3. 03/02/052005 Big XII IS Symposium- OU3 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. 03/02/052005 Big XII IS Symposium- OU4 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. 03/02/052005 Big XII IS Symposium- OU5 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. 03/02/052005 Big XII IS Symposium- OU6 The Definition and Process of interruption Interrupt arrives IL + Interrupt processing Interrupt departs Recall time- RL Pre-processingPost-processing Definition- (Corragio, 1990) According to distraction theory, interruption is “an externally generated, randomly occurring, discrete event that breaks continuity of cognitive focus on a primary task.” Process- (Trafton, 2003)

7. 03/02/052005 Big XII IS Symposium- OU7 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)

8. Email Strategies Response Processing Frequency Prioritization Categorization & Organization Archiving & Storage Message Structure & Form Contextualization Affectivity Perspective Taking Attention Taking, etc. Email and Other Task Performance  % increase in worker utilization  No. of interruptions per task.  Add. time spent due to interruptions  Email response time  Primary task completion time. Individual characteristics - Age - Gender - Experience - Cognitive Style - Personality - Attitude A Framework Studying Email Processing Strategies Adapted from Te’eni (2001) & Speier et al. (2003) Interruptive Work Environment  Email and Primary Task Characteristics -Arrival Frequency -Arrival Pattern -Message Form o Size o Distribution o Organization o Formality -Content Complexity o Cognitive o Dynamic o Affective  Task Situation o Analyzability o Variety o Temporal Demands Sender Receiver Distance o Cognitive o Affective Workload Level  Dependency on Email Communication  Cultural Values & Norms  Work Role  Goal  Various Social Dimensions  Other Org. Factors

9. 03/02/052005 Big XII IS Symposium- OU9 Research Questions RQ1: What is the optimal email processing strategy? RQ1: What is the optimal email processing strategy? RQ2: Is the optimal policy robust across all work environments? RQ2: Is the optimal policy robust across all work environments?

10. 03/02/052005 Big XII IS Symposium- OU10 Approach/Methodology Discrete Event Simulation Discrete Event Simulation Difficulty in getting the subject for such study. Difficulty in getting the subject for such study. Can serve as a tool for theory enquiry and development (Peschl, 2001; Di Paolo, 2000). Can serve as a tool for theory enquiry and development (Peschl, 2001; Di Paolo, 2000). Demonstrate the use of a design science paradigm (Hevner, 2004) Demonstrate the use of a design science paradigm (Hevner, 2004) Another way of doing thought experiments. Another way of doing thought experiments. Hypotheses development using simulation () Hypotheses development using simulation () A technique that can often give surprising ‘emerging’ results. A technique that can often give surprising ‘emerging’ results.

11. 03/02/052005 Big XII IS Symposium- OU11 Approach/Methodology Study conducted in two phases Study conducted in two phases Model simplicity- Helps in replication & extension (Axelrod, 2003 & Pidd, 1996) Model simplicity- Helps in replication & extension (Axelrod, 2003 & Pidd, 1996) Guidelines for good model development (Chwif et al. 2000) Guidelines for good model development (Chwif et al. 2000) “divide your model into parts and model each part separately creating a series of simpler models instead of one ‘huge’ one” and “only after you validate, analyze and have the results, add more complexity if you feel it is really necessary.” “divide your model into parts and model each part separately creating a series of simpler models instead of one ‘huge’ one” and “only after you validate, analyze and have the results, add more complexity if you feel it is really necessary.”

12. 03/02/052005 Big XII IS Symposium- OU12 Phase-I (P-I) 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

13. 03/02/052005 Big XII IS Symposium- OU13 Verification and Validation of model Two methods proposed by Sargent (2003): Two methods proposed by Sargent (2003): “subjective decision of modeling team” approach and “subjective decision of modeling team” approach and “IV & V” (independent verification and validation) approach. “IV & V” (independent verification and validation) approach. Specifically, we used animation techniques, degenerate tests, event validity, face validity, internal validity and a fixed values approach (Sargent, 2003) to rigorously verify and validate our models. Specifically, we used animation techniques, degenerate tests, event validity, face validity, internal validity and a fixed values approach (Sargent, 2003) to rigorously verify and validate our models.

14. 03/02/052005 Big XII IS Symposium- OU14 P-I Result analysis cont… Profile plots E ffect of Policy x Workload Level on % increase in Utilization Effect of Policy x Task Complexity on % increase in Utilization.

15. 03/02/052005 Big XII IS Symposium- OU15 P-I Result analysis cont… Profile plots E ffect of Policy x Workload Level on # of interruptions per simple task. Effect of Policy x Task Complexity on # of interruptions per simple task.

16. 03/02/052005 Big XII IS Symposium- OU16 P-I Result analysis cont… Profile plots Effect of Policy on mean completion time of simple tasks Effect of Policy on mean response time of emails

17. 03/02/052005 Big XII IS Symposium- OU17 Phase II (P-II) 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. 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

18. 03/02/052005 Big XII IS Symposium- OU18 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%

19. 03/02/052005 Big XII IS Symposium- OU19 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

20. 03/02/052005 Big XII IS Symposium- OU20 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

21. 03/02/052005 Big XII IS Symposium- OU21 Results (a) Percent Increase in Utilization

22. 03/02/052005 Big XII IS Symposium- OU22 Results (b) Additional Time (min) spent per day due to interruptions

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

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

25. 03/02/052005 Big XII IS Symposium- OU25 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

26. 03/02/052005 Big XII IS Symposium- OU26 Practical Significance Use of C2 or C4 policy saves approx. 17min/day per knowledge worker = 3 to 4% Use of C2 or C4 policy saves approx. 17min/day per knowledge worker = 3 to 4% Total overhead per year with C2 or C4 policy for a mid size organization having 100 KW earning average salary of 5,000$ = ??? Total overhead per year with C2 or C4 policy for a mid size organization having 100 KW earning average salary of 5,000$ = ???

27. 03/02/052005 Big XII IS Symposium- OU27 Limitations of the model Assumptions of the model are its limitations Assumptions of the model are its limitations Knowledge worker works strictly from 8 to 12 and then from 1 to 5pm. Need for relaxing the work schedule! Knowledge worker works strictly from 8 to 12 and then from 1 to 5pm. Need for relaxing the work schedule! 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 an interruptible primary task. In reality, not all primary tasks are interruptible. For e.g. group meetings KW is working on an 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 interruptible in current model. Emails are not interruptible in current model.

28. 03/02/052005 Big XII IS Symposium- OU28 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 for e.g. blackberries. Modeling by incorporating more doses of reality. Considering other communication media along with email for e.g. blackberries. http://iris.okstate.edu/rems/ Suggestions or comments or Questions????