REDUCING EMAIL OVERLOAD

1. REDUCING EMAIL OVERLOAD DECISION SUPPORT FOR KNOWLEDGE WORKERS

2. AGENDA • INTRODUCTION • RESEARCH MISSION, GOALS, STRATEGY, & OBJECTIVES • CALL CENTER RESEARCH • EXAMPLES & RESULTS OF INTEREST • QUEUING THEORY • SINGLE SERVER • MULTI SERVER • SIMULATION • FUTURE RESEARCH • QUESTIONS & COMMENTS

3. INTRODUCTION • INFORMATION OVERLOAD • Information overload can be defined as receiving more information than can possibly be processed (Butcher, 1998). Information received at a rate too high for the receiver to process efficiently causes distractions, stress, and increases in errors (Klapp, 1986). • “The world’s total yearly production of print, film, optical, and magnetic content would require roughly 1.5 billion GB of storage. This is the equivalent of 250 MB per person for each man, woman, and child on earth” (Varian and Lyman, 2000)

4. INTRODUCTION • KNOWLEDGE WORKER • “True, knowledge workers are still a minority, but they are fast becoming the largest single group. And they have already become the major creator of wealth.” (Drucker, 2002) • EMAIL OVERLOAD • “More than 1 million messages pass through the Internet every hour. An estimated 2.7 trillion e-mail messages were sent in 1997.” And it was projected that nearly 7 trillion messages would be sent in 2000 (Overly, Foley & Lardner, 1999). • Intel (1999 Intel Employee Email Use Survey) • 200: average number of emails waiting in an employee’s inbox • 2.5: average number of hours of each day employees spend managing email • 30: percentage of email that is unnecessary

5. RESEARCH STREAMS • MISSION • IMPROVEMENT OF KNOWLEDGE WORK • GOALS • DECISION SUPPORT FOR KNOWLEDGE WORKERS • STRATEGY • MODELING AND MANIPULATION OF EMAIL PROCESSING SCHEMES • OBJECTIVES • DISCOVERY OF HEURISTICS & CONTINGENCIES • VALIDATION OF HEURISTICS & CONTINGENCIES • IMPLEMENTATION • DSS • ES • INTELLIGENT AGENTS

6. SCENARIO/POLICY TABLEEXAMPLE

7. QUEUING THEORYEMAIL ANALOGIES • SERVER → KNOWLEDGE WORKER • CUSTOMER → EMAIL • QUEUE → INBOX • WAIT IN THE SYSTEM → RESPONSE TIME • QUEUING DISCIPLINE → PROCESSING SCHEME

8. QUEUING THEORY

9. CALL CENTER RESEARCHGans, N., Koole, G., and Mandelbaum, A. (2002)Whitt (2002) • IMPATIENCE, ABANDONMENT, & RETRIALS • CALL MIXING • LACKING COMBINATIONS OF ABOVE • LACKING ITERATIVE ASPECT OF EMAIL • LACKING INTERACTION ASPECT OF EMAIL

10. SINGLE SERVER QUEUE EXAMPLEA FACULTY MEMBER’S WEEKLY EMAIL

11. SINGLE SERVER QUEUE EXAMPLEA FACULTY MEMBER’S EMAIL • ASSUMPTIONS • FIFO • EXPONENTIAL INTERARRIVAL AND PROCESSING TIMES • RAQS (Kamath, et. al., 1999) • UTILIZATION: 0.952 • PERCEIVED INFORMATION OVERLOAD???

12. SINGLE SERVER QUEUE EXAMPLEA FACULTY MEMBER’S EMAIL

13. SINGLE SERVER QUEUE EXAMPLEA FACULTY MEMBER’S EMAIL

14. MULTI-SERVER QUEUES EXAMPLEA KNOWLEDGE NETWORK

15. MULTI-SERVER QUEUES EXAMPLEA KNOWLEDGE NETWORK • ASSUMPTIONS • FIFO • POISON ARRIVALS • EXPONENTIAL PROCESSING TIME DISTRIBUTIONS • UTILIZATIONS • REP 1: 0.80 • REP 2: 0.86 • REP 3: 0.81 • AVERAGE TIME IN THE SYSTEM • 0.4356 DAYS

16. SIMULATION OF A KNOWLEDGE WORKER

17. SIMULATION OF A KNOWLEDGE WORKER • PARAMETERS • ARRIVALS • APPLICATIONS: EXPONENTIAL WITH A MEAN OF 2 HOURS BETWEEN ARRIVALS • INQUIRIES: EXPONENTIAL WITH A MEAN OF 1 HOUR BETWEEN ARRIVALS • PROCESSING • APPLICATIONS: TRIANGULAR (0, 0.1., 0.2) • INQUIRIES: TRIANGULAR (0, 0.041, 0.082) • OUTSIDE WORK • AVERAGE DURATION OF .67 HRS • AVERAGE TIME BETWEEN FAILURES OF .33 HRS • RESOLUTIONS • APPLICATIONS: 75% • INQUIRES: 90%

18. SIMULATION OF A KNOWLEDGE WORKER

19. FUTURE RESEARCH • CONTINUED MODELING • For purposes of dissertation, partial completion of Scenario/Policy Table • VALIDATION • CASE STUDY: For purposes of dissertation, validation of scenarios depicted in the Scenario/Policy Table within the domain of the graduate college • IMPLEMENTATION • DSS • ES • INTELLIGENT AGENTS • BEHAVIORIAL ASPECTS • Perceived Information Overload

20. QUESTIONS & COMMENTS???