Scheduling Email communication to reduce Information Overload and Interruptions

1. Scheduling Email communication to reduce Information Overload and Interruptions Oh ! I dream of an ideal workplace - “an interrupt-free workplace” By Ashish Gupta Ramesh Sharda Robert Greve Manjunath Kamath

2. Objective of the study • To improve knowledge worker performance by identifying policies that will :- • Reduce information overload due to emails. • Reduce interruption effect due to emails. • To develop a heuristic chart to guide a knowledge worker on email response scheduling. • Validate the results of prior research. 1

3. Prior relevant research on Email Overload & interruptions research • First reportedby Peter Denning (1982),Later by Hiltz, et. al. (1985), Whittaker, et. al.(1996) and many… • 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). • Research done HCI is rich but in MS/OR??? • Research that looks at the problem of information overload and interruptions simultaneously is scarce. (Speier,et.al.1999, Jackson, et.al., 2003, 2002, 2001), Venolia et.al. (2003) 2

4. Recall time- RL IL + Interrupt processing Pre-processing Post-processing Interrupt arrives Interrupt departs Our approach- SIMULATION • Phases of task processing • (Miyata & Norman, 1986):- • Planning • Execution • Evaluation • Policies that we are comparing :- • Triage:(C1-morning, C1-Afternoon) • Scheduled:(C2, C4, C8(jackson, et. al.2003)) • Flow (continuous):C 3

5. Resource utilization Resource utilization change Email Policy Number of Interruptions per task Task Complexity mix Interrupt arrival pattern Task Completion time Research Model *Utilization: Probability of a knowledge worker being busy (λ/µ) 4

6. Hypothesis Formulation • H1: Knowledge worker utilization increases with the increase in the frequency of email hour slots for all levels of task Complexity mix, Resource utilization and Interrupt arrival patterns. • H2: The average number of times a simple or complex task gets interrupted increases with the increase in the frequency of email-hour slots, for all levels of Task complexity mix, Resource Utilization and Interrupt arrival patterns. • H3: Average completion time for simple or complex tasks increases with the increase in the frequency of email-hour slots, for all levels of Task complexity mix, Resource Utilization and Interrupt arrival patterns.

7. Model Implementation Sn, Cn- new simple & complex task Si, Ci – interrupted simple & complex task E – Email (Interrupt) 7

8. Dependent variables % change in utilization # of interruptions per simple task # of interruptions per complex task Average completion time for simple task Average completion time for complex task Effect Policy (P) Workload Level (RU) Task Complexity (TC) Interrupt Arrival Pattern (IAP) P * RU P * TC P * IAP P * RU * IAP P * TC * IAP P * RU * TC P * RU * TC * IAP Statistical analysis of Simulation results 8

9. Result analysis cont… Profile plots RU 9

10. Result analysis cont… Profile plots RU 10

11. Result analysis cont… Profile plots RU 11

12. Contigency framework for Email management 12

13. Practical implications • If other tasks are more important and email communication is secondary ! • Check emails 4 times a day with each processing not exceeding 45 min if you want to be most productive. • Is timely email processing survival issue for your kind of organization? • Use flow (continuous) policy • Use our prescription chart. 13

14. Future research • Perform the study in experimental or field settings. • Use perceived measures of Information overload (NASA-TLX, SWAT) • More realistic modeling by incorporating email characteristics • More discrete policies • Use of other frameworks for task analysis • User-interface development Suggestions or comments or Questions???? 14