An Analysis of Email Response Policies under Different Arrival Patterns By Ashish Gupta Ramesh...

An Analysis of Email Response Policies under An Analysis of Email Response Policies under Different Arrival PatternsDifferent Arrival Patterns

By By

Ashish GuptaAshish Gupta Doctoral Student, Department of Management Science & Information Systems,

Oklahoma State University, Stillwater.

Ramesh Sharda Ramesh Sharda Regents Professor of Management Science & Information Systems,

Director, Institute for Research in Information Systems, Oklahoma State University, Stillwater.

01/06/0501/06/05 ICS-2005ICS-2005 22

Objective of the studyObjective of the study

To improve individual knowledge worker To improve individual knowledge worker performance by identifying policies that will :-performance by identifying policies that will :-

To model email work environment by considering To model email work environment by considering various email characteristics.various email characteristics.

Improve response time of emails and primary task Improve response time of emails and primary task completion timecompletion time

Reduce number of interruptions Reduce number of interruptions

Validate the results of prior research.Validate the results of prior research.

01/06/0501/06/05 ICS-2005ICS-2005 33

Problem significanceProblem significance

20042004 AMA Research on w AMA Research on workplace E-Mail & Productivityorkplace 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-Osterman Research- How often do you How often do you

check your E-mail for new messages check your E-mail for new messages

when at work?when at work?

01/06/0501/06/05 ICS-2005ICS-2005 44

Problem significanceProblem significance

E-Policy Institute (2004)E-Policy Institute (2004) Annual Email growth rate= 66 %Annual Email growth rate= 66 %

Corporate ResearchCorporate Research IBM, Microsoft, Xerox, Ferris, Radicati, etc.IBM, Microsoft, Xerox, Ferris, Radicati, etc.

Need for more research in MS/IS thatNeed for more research in MS/IS that Looks at the problem of information overload and Looks at the problem of information overload and

interruptions simultaneously.interruptions simultaneously.

01/06/0501/06/05 ICS-2005ICS-2005 55

Extant ResearchExtant Research

Overload due to emails-Overload due to emails- First reportedFirst reported byby Peter Denning Peter Denning (1982). (1982).

Most recently reported byMost recently reported by Ron Weber (MISQ, Ron Weber (MISQ, Editor-in-Chief 2004)Editor-in-Chief 2004)

Interruptions due to emails-Interruptions due to emails-Reported by someReported by some- Speier,et.al.1999, Jackson, et.al., - Speier,et.al.1999, Jackson, et.al., 2003, 2002, 2001), Venolia et.al. (2003) 2003, 2002, 2001), Venolia et.al. (2003)

01/06/0501/06/05 ICS-2005ICS-2005 66

Extant ResearchExtant 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 Email as a medium of managerial choice”, M. Markus

(1994)(1994) ““You have got (Lots and Lots) of mail” in “The Attention You have got (Lots and Lots) of mail” in “The Attention

Economy” by Davenport (2001)Economy” by Davenport (2001) ““The Time Famine: Towards a Sociology of Work Time”, The Time Famine: Towards a Sociology of Work Time”,

Leslie Perlow (1999)Leslie Perlow (1999)

01/06/0501/06/05 ICS-2005ICS-2005 77

Phenomenon of InterruptionPhenomenon of Interruption

Interrupt arrives

IL + Interrupt processing

Interrupt departs

Recall time- RLPre-processing Post-processing

Interruptions-Interruptions- According to According to distraction theorydistraction theory, interruption is , interruption is “an “an externally generated, randomly occurring, discrete eventexternally generated, randomly occurring, discrete event that breaks continuity of cognitive focus on a primary task“ that breaks continuity of cognitive focus on a primary task“ (Corragio, 1990; Tétard F. 2000).(Corragio, 1990; Tétard F. 2000).

01/06/0501/06/05 ICS-2005ICS-2005 88

Previous Research Model Previous Research Model

Performance Measures1. % Increase in utilization2. Number of interruptions per task. 3. Primary task completion time4. 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 LevelLow vs. Medium vs. High

Email PolicyFlow vs.

Scheduled vs.Triage

Only “high” dependency on email communication (3 hrs) with exponential email arrivals was studied

01/06/0501/06/05 ICS-2005ICS-2005 99

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 characteristicsProcessing 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

01/06/0501/06/05 ICS-2005ICS-2005 1010

Email typesEmail types

Emails differentiated on the basis of its ‘content’ Emails differentiated on the basis of its ‘content’ or the ‘action required by the user’or the ‘action required by the user’Notation Email type Discrete arrival

percentage

1 Priority email 5%

2 Spam 5%

3 Informative email 20%

4 Email with non-diminishingservice time

55%

5 Email with diminishingservice time

15%

01/06/0501/06/05 ICS-2005ICS-2005 1111

Email PoliciesEmail Policies

Dependency on Email Communication

Policy type Very Low(1 hr)

Low (2 hrs)

High (3 hrs)

Very High (4 hrs)

Notation # of Emailhour- slots

Triage 8am-9am 8am-10am 8am-11am 8am -12 noon C1 1

Schedule 8am-8:30am4:30pm- 5pm

8am-9am4pm-5pm

8am-9:30am 3:30 am to 5:00

pm

8am-10am3pm- 5pm

C2 2

Schedule 8am-8:15am,11am-11:15am1pm-1:15pm4:45pm- 5pm

8am-8:30am,11am-11;30am1pm-1:30pm4:30pm- 5pm

8am-8:45 am, 11am-11:45am,1 pm - 1:45 pm, 4:15 pm - 5:00

pm

8am-9am11am - 121pm- 2pm4pm- 5pm

C4 4

Schedule 8am-8:08am9- 9:08amand so on

8-8:15am9-9:15am

10-10:15amand so on

8-8:23am9-9:23am

10-10:23am and so on

8- 8:30am9- 9:30pm

10- 10:30pmand 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 NotApplicable

01/06/0501/06/05 ICS-2005ICS-2005 1212

MethodologyMethodology

Discrete event simulation using Arena 8.01Discrete event simulation using Arena 8.01

Model Run length= Model Run length= 500500 days days

Model Warm-up time= Model Warm-up time= 5050 days days

No. of replications of each model= No. of replications of each model= 2020

1616 scenarios evaluated for scenarios evaluated for 55 different policies. different policies.

Thus, Total number of simulations models= Thus, Total number of simulations models= 16 x 5= 8016 x 5= 80

Total number of data points generated

= 80 x 20 = = 80 x 20 = 16001600

01/06/0501/06/05 ICS-2005ICS-2005 1313

ScenariosScenarios

Scenarios Email (E) dependency E Arrival pattern E processing time

1 Very low Time stationary Expo Small

2 Very low Time stationary Expo Large

3 Very low Non-Stationary Expo Small

4 Very low Non-Stationary Expo Large

5 Low Time stationary Expo Small

6 Low Time stationary Expo Large

7 Low Non-Stationary Expo Small

8 Low Non-Stationary Expo Large

9 High Time stationary Expo Small

10 High Time stationary Expo Large

11 High Non-Stationary Expo Small

12 High Non-Stationary Expo Large

13 Very High Time stationary Expo Small

14 Very High Time stationary Expo Large

15 Very High Non-Stationary Expo Small

16 Very High Non-Stationary Expo Large

01/06/0501/06/05 ICS-2005ICS-2005 1414

ParametersParameters

S #

Type 4 email (E)

Processing

time (PT)

Type 5E PT(min)

Total Email PT per day

Avg. EmailArrival Rate

Primary Task

(P) ArrivalRate /day E Util P Util

Min(E+P)Util

1 5 5 1 12 62 0.125 0.775 0.9

2 15 15 1 5 62 0.125 0.775 0.9

3 5 5 1 12 62 0.125 0.775 0.9

4 15 15 1 5 62 0.125 0.775 0.9

5 5 5 2 24 52 0.25 0.65 0.9

6 15 15 2 10 52 0.25 0.65 0.9

7 5 5 2 24 52 0.25 0.65 0.9

8 15 15 2 10 52 0.25 0.65 0.9

9 5 5 3 36 42 0.375 0.525 0.9

10 15 15 3 15 42 0.375 0.525 0.9

11 5 5 3 36 42 0.375 0.525 0.9

12 15 15 3 15 42 0.375 0.525 0.9

13 5 5 4 48 32 0.5 0.4 0.9

14 15 15 4 20 32 0.5 0.4 0.9

15 5 5 4 48 32 0.5 0.4 0.9

16 15 15 4 20 32 0.5 0.4 0.9

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)

01/06/0501/06/05 ICS-2005ICS-2005 1515

Bird’s Eye view of Entire Bird’s Eye view of Entire model built using Arenamodel built using Arena

f or t ypes 2 3 4em ail s t r at egy

im plem ent ing

k W

Pr eem pt1 2 3 4

Pr o c e s s em a il

t im ee m ail e f f ec t iv e

nonof f ice hour shold dur ing

Emai l hours Non Emai l hours Knowledge Worker (Busy- green, Idle- whi te, Inactive- hatched)

t y p eAs s ign e m a ile m ails

E ls e

e m a il t y p e = = 1e m a il t y p e < = 4

t y p e o f em a il

f or t ype 5em ail s t r at egy

im plem ent ing

k W

Pr eem pt e m ail 5 in 1T r u e

F a ls e

t y p e 5d e c id in g s er v ic e t im e o f

s p e nte m ail t y p e 5 t im e

E ls e

e m a il t y p e = = 1e m a il t y p e = = 2e m a il t y p e = = 3

s p lit e m a il t y p e





s im plep la n n in g s im p le p la n n in g o r n o t s im p le

I n t er r u p t e d d u r ingT r u e

F a ls e

e x e c u t io n s im p le

s im pleRL p la n n in g

T r u e

F a ls e

e x e c u t io n o r n o t s im p leI n t er r u p t e d d u r ing

s im plee v a lua t io n

T r u e

F a ls e

e v a lua t io n o r n o t s im p leI n t er r u p t e d d u r ing

Dis p os e s im p le

s im pleRL ex e c u t in g

s im pleRl e v a lu a t ing

Hold s im ple

s im pleI L p la n n in g

s im pleI L ex e c u t in g

s im pleI L ev a lu a t in g

As s ign 1 9 t im et a s k c o m p le t io na v e r a g e s im p le

s e t p la n n ing RT0

s e t e x e c ut io n RT0RT0

s e t e v a lu at io n

As s ign 2 3

As s ign 2 4As s ign 2 5

k W

Pr eem pte m ail 5 in 2

wo r k d a yin t e r r u p t ion s p e r

Av e r a g e

in t e r r u p t ion s s pn o o f e x e s

in t e r r u p t ion sn o o f s e v a l

in t e r r u p t ion sn o o f

r e s po n s e t im ea v e r a g e em a il Dis p os e e m a il

r e s po n s e t im em a x im u m e m a il

r e s po n s e t im em in im u m e m a il

r e s po s e t im ed e v ia t io n in e m a il

s t a nd a r d

00:00:00

0

Jan u ary 1 , 2 0 0 5

0

0

0

0

0 0

0

0 0

0

0

0 0

0

00

0 0

0

0 0

0

0

Zoom in follows….

Arena Email flow SnapshotArena Email flow Snapshot

1

2

for types 2 3 4email strategyimplementing

kW

Preempt1234

Process email

Else

email type==1email type==2email type==3

split email ty pe

0

1

2

Preempts the KW when an email of type 1 arrives during email hrs . Stores remaining processing time in an attribute ‘RT’

timeemail effective

nonoffice hourshold duringtype

Assign emailemails

Else

email type==1email type<=4

type of email

0

3

for type 5email strategyimplementing

kW

Preempt email 5 in 1True

False

type 5deciding service time of

spentemail type 5 time

kW

Preemptemail 5 in 2

0

0

0

0

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

01/06/0501/06/05 ICS-2005ICS-2005 1717

Arena Primary Task SnapshotArena Primary Task Snapshot

simpleplanning s imple planning or not s imple

Interrupted duringTrue

False

simpleRL planning

Hold simple

simpleIL planning

Assign 19

set planning RT0Assign 23 interruptions sp

no of

0 0

0

0

0 0

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

01/06/0501/06/05 ICS-2005ICS-2005 1919

Model LogicModel LogicNew 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 ≤ 1Calculate;RL = {RT * [χ * *( K-1)] * [ (1- χ)* * ( L-1 ) } / Beta (K,L)

01/06/0501/06/05 ICS-2005ICS-2005 2020

Model LogicModel Logic For K = 2, L = 1;For K = 2, L = 1; Calculate;Calculate;

T1 = IL + Tb + RL; T1 = IL + Tb + RL; Seize KW for time T1;Seize KW for time T1;

Process PiProcess PiSet RT=0;Set RT=0;

Release KW;Release KW;If If ii = = 2 || 3 || 4 || 5,2 || 3 || 4 || 5,

Step.3 Step.3 Release Release Ei, Ei, ifif{(STATE(dummy) == IDLE_RES && {(STATE(dummy) == IDLE_RES && Process email 1234.WIP == 0 && Process email 1234.WIP == 0 && email 5 in 1.WIP == 0 && email 5 in 1.WIP == 0 && email 5 in 2.WIP == 0 ) || email 5 in 2.WIP == 0 ) || ( STATE(anti dummy) == IDLE_RES && ( STATE(anti dummy) == IDLE_RES && Primary.WIP == 0 && Primary.WIP == 0 && NQ(Hold primary.Queue) == 0 && NQ(Hold primary.Queue) == 0 && IL Primary .WIP=0 && IL Primary .WIP=0 && RL primary.WIP == 0 ) } =RL primary.WIP == 0 ) } = TRUETRUE

Else Hold;Else Hold;

01/06/0501/06/05 ICS-2005ICS-2005 2121

Model logic- commentsModel logic- commentsIf New arrival = PnIf New arrival = Pn

Step4.Step4. Release if, Release if,STATE(kW) == IDLE_RES;STATE(kW) == IDLE_RES;

Else Hold;Else Hold;

//*****//*****Tb- Value added time spent on the task Tb- Value added time spent on the task BBeforeefore interruption interruptionTa- Value added time spent on the task Ta- Value added time spent on the task AAfterfter interruption interruption χ - Fraction of task completed before interruption occurredχ - Fraction of task completed before interruption occurredIL – Interruption LagIL – Interruption LagRL – Resumption LagRL – Resumption LagPi – interrupted primary taskPi – interrupted primary taskDummy resource- implements email hoursDummy resource- implements email hoursAnti-dummy resource – implements non- email hoursAnti-dummy resource – implements non- email hours *****//*****//

Stop;Stop;Stop;

ResultsResults

(a) Percent Increase in Utilization

% increase in utilization (base value=0.9)

POLICY

CC8C4C2C1

Est

ima

ted

Ma

rgin

al M

ea

ns

9

8

7

6

5

4

3

2

1

Email dependency

high

low

very high

very low


POLICY

CC8C4C2C1

Est

ima

ted

Ma

rgin

al M

ea

ns

8

7

6

5

4

3

2

1

Email Arriv. Pattern

Expo

Non Stationary Expo


POLICY

CC8C4C2C1

Est

ima

ted

Ma

rgin

al M

ea

ns

9

8

7

6

5

4

3

2

EPT

large

Small

ResultsResults

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

Additional Time spent / day due to interruptions

POLICY

CC8C4C2C1

Est

ima

ted

Ma

rgin

al M

ea

ns

40

30

20

10

0

Email Dependency

high

low

very high

very low


POLICY

CC8C4C2C1

Est

ima

ted

Ma

rgin

al M

ea

ns

40

30

20

10

0


Expo

Non-Stationary Expo


POLICY

CC8C4C2C1

Est

ima

ted

Ma

rgin

al M

ea

ns

40

30

20

10

Email Processing Tim

large

Small

01/06/0501/06/05 ICS-2005ICS-2005 2424

Response time resultsResponse 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]

ResultsResultsEmail wait time

POLICY

CC8C4C2C1

Est

imat

ed M

argi

nal M

eans

400

300

200

100

0

Email Dependency

high

low

very high

very low

Email wait time

POLICY

CC8C4C2C1

Estim

ate

d M

arg

ina

l M

ea

ns

400

300

200

100

0

Email Pattern

EA

NSEA

Email wait time

POLICY

CC8C4C2C1

Est

ima

ted

Ma

rgin

al M

ea

ns

400

300

200

100

0

EPT

large

Small

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

time

ResultsResultsNon-Value Added time (RL+ IL) spent

per Primary Task (min)

POLICY

CC8C4C2C1

Est

imat

ed M

argi

nal M

eans

1.0

.8

.6

.4

.2

0.0

Email Dependency

high

low

very high

very low

Non-Value Added time (RL+ IL) spent


POLICY

CC8C4C2C1

Estim

ate

d M

arg

ina

l M

ea

ns

.8

.7

.6

.5

.4

.3

.2

.1


Expo

Non-Stationary Expo

Non-Value Added time (RL+ IL) spent


POLICY

CC8C4C2C1

Est

ima

ted

Ma

rgin

al M

ea

ns

1.0

.8

.6

.4

.2

0.0

Email Processing tim

large

Small

(d.1) Avg. Additional time spent (wasted) in recalling and

switching for processing one primary task

ResultsResults

(d.2) Average Primary Task Wait Time

Avg Primary Task Wait time (min)

POLICY

CC8C4C2C1

Est

ima

ted

Ma

rgin

al M

ea

ns

2000

1000

0

Email Dependency

high

low

very high

very low

Avg. Primary Task Wait Time (min)

POLICY

CC8C4C2C1

Est

ima

ted

Ma

rgin

al M

ea

ns

1600

1400

1200

1000

800

600

400

200

0


Expo

Non-Stationary Expo

Avg Primary Task Wait time (min)

POLICY

CC8C4C2C1

Est

ima

ted

Ma

rgin

al M

ea

ns

2000

1000

0


large

Small

ResultsResultsAvg Primary Task Completion time

POLICY

CC8C4C2C1

Est

ima

ted

Ma

rgin

al M

ea

ns

2000

1000

0

Email Dependency

high

low

very high

very low

Avg Primary Task Completion time

POLICY

CC8C4C2C1

Est

ima

ted

Ma

rgin

al M

ea

ns

1800

1600

1400

1200

1000

800

600

400

200

0


EA

NSEA

(d.3) Average Primary Task Completion Time

Avg Primary Task Completion time

POLICY

CC8C4C2C1

Est

ima

ted

Ma

rgin

al M

ea

ns

2000

1000

0


large

Small

01/06/0501/06/05 ICS-2005ICS-2005 2929

Optimal Policy ??Optimal Policy ??

Previous research found C4 as the optimal policy (no Previous research found C4 as the optimal policy (no consideration was given to email arrival pattern and consideration was given to email arrival pattern and characteristics).characteristics).

Current Research found under varying email arrival Current Research found under varying email arrival characteristics-characteristics- Optimal policy for primary task completion time - C1 & Optimal policy for primary task completion time - C1 &

C2 closely followed by C4.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 Optimal policy for reducing interruptions- C1& C4 closely

followed by C2followed by C2

01/06/0501/06/05 ICS-2005ICS-2005 3030

Limitations of the modelLimitations of the model

Assumptions of the model are its limitationsAssumptions of the model are its limitations Knowledge worker works strictly according from 8 to 12 Knowledge worker works strictly according from 8 to 12

and then from 1 to 5pm. Need for relaxing the work-hrs.and then from 1 to 5pm. Need for relaxing the work-hrs. Knowledge worker is busy only 90% of the time in a given Knowledge worker is busy only 90% of the time in a given

workday.workday. KW is working on interruptible primary task. In reality, not KW is working on interruptible primary task. In reality, not

all primary tasks are interruptible. For e.g. group meetingsall 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.

01/06/0501/06/05 ICS-2005ICS-2005 3131

Limitations & future Limitations & future researchresearch

Perform the study in field or experimental Perform the study in field or experimental settings.settings.

Modeling utility/ life of an email.Modeling utility/ life of an email. Modeling group knowledge network and at Modeling group knowledge network and at

organizational level. organizational level. Modeling by incorporating more doses of Modeling by incorporating more doses of

reality. Considering other communication media reality. Considering other communication media along with email.along with email.

http://iris.okstate.edu/rems/http://iris.okstate.edu/rems/Suggestions or comments or Questions????Suggestions or comments or Questions????

An Analysis of Email Response Policies under Different Arrival Patterns By Ashish Gupta Ramesh...

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