By Ashish Gupta Ramesh Sharda Robert Greve Manjunath Kamath Mohanraj Chinnaswamy How often should we...

How often should we check our email? Balancing interruptions and quick response times

By By

Ashish Gupta Ashish Gupta

Ramesh Sharda Ramesh Sharda Robert GreveRobert Greve

Manjunath KamathManjunath KamathMohanraj ChinnaswamyMohanraj Chinnaswamy

03/02/0503/02/05 2005 Big XII IS Symposium- OU 2005 Big XII IS Symposium- OU 22

Objective of the studyObjective of the study

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

By improving email response time & primary task completion By improving email response time & primary task completion time.time.

Reduce number of interruptions.Reduce number of interruptions. Validate the results of prior research.Validate the results of prior research. To model email work environment by considering To model email work environment by considering

various email characteristicsvarious email characteristics


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?


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.


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)


The Definition and Process of The Definition and Process of interruptioninterruption

Interrupt arrives

IL + Interrupt processing

Interrupt departs

Recall time- RLPre-processing Post-processing

Definition-Definition- (Corragio, 1990)(Corragio, 1990) According to According to distraction theorydistraction theory, , interruption is “an interruption is “an externally generated, randomly occurring, externally generated, randomly occurring, discrete eventdiscrete event that breaks continuity of cognitive focus on a that breaks continuity of cognitive focus on a primary task.”primary task.”ProcessProcess-- (Trafton, 2003) (Trafton, 2003)


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)

Email StrategiesResponse Processing Frequency

PrioritizationCategorization & Organization

Archiving & StorageMessage Structure & Form

ContextualizationAffectivity

Perspective TakingAttention Taking, etc.

Email and Other Task Performance

% increase in worker utilizationNo. of interruptions per task. Add. time spent due to interruptionsEmail response timePrimary task completion time.

Individual characteristics-Age-Gender-Experience-Cognitive Style-Personality-Attitude

A Framework Studying Email Processing StrategiesAdapted from Te’eni (2001) & Speier et al. (2003)

Interruptive Work

Environment

Email and Primary Task Characteristics-Arrival Frequency-Arrival Pattern-Message FormoSizeoDistributionoOrganizationoFormality

-Content ComplexityoCognitiveoDynamicoAffective

Task SituationoAnalyzabilityoVarietyoTemporal Demands

•Sender Receiver Distanceo Cognitiveo Affective

•Workload LevelDependency on

Email CommunicationCultural Values & NormsWork RoleGoalVarious Social DimensionsOther Org. Factors


Research QuestionsResearch Questions

RQ1: What is the optimal email processing RQ1: What is the optimal email processing strategy?strategy?

RQ2: Is the optimal policy robust across all RQ2: Is the optimal policy robust across all work environments?work environments?


Approach/MethodologyApproach/Methodology

Discrete Event SimulationDiscrete 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, Can serve as a tool for theory enquiry and development (Peschl,

2001; Di Paolo, 2000).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.

ashish gupta


Approach/MethodologyApproach/Methodology

Study conducted in two phasesStudy conducted in two phases Model simplicity- Helps in replication & extension Model simplicity- Helps in replication & extension

(Axelrod, 2003 & Pidd, 1996)(Axelrod, 2003 & Pidd, 1996) Guidelines for good model development (Chwif et al. Guidelines for good model development (Chwif et al.

2000)2000) “ “divide your model into parts and model each part separately divide your model into parts and model each part separately

creating a series of simpler models instead of one ‘huge’ one” creating a series of simpler models instead of one ‘huge’ one” and “only after you validate, analyze and have the results, add and “only after you validate, analyze and have the results, add more complexity if you feel it is really necessary.” more complexity if you feel it is really necessary.”


Phase-I (P-I) Research Model Phase-I (P-I) 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


Verification and Validation of Verification and Validation of modelmodel

Two methods proposed by Sargent (2003):Two methods proposed by Sargent (2003): ““subjective decision of modeling team” approach andsubjective decision of modeling team” approach and “ “IV & V” (independent verification and validation) IV & V” (independent verification and validation)

approach. approach.

Specifically, we used animation techniques, Specifically, we used animation techniques, degenerate tests, event validity, face validity, internal degenerate tests, event validity, face validity, internal validity and a fixed values approach (Sargent, 2003) validity and a fixed values approach (Sargent, 2003) to rigorously verify and validate our models. to rigorously verify and validate our models.

ashish gupta

read this


P-I Result analysis cont… P-I Result analysis cont… Profile plotsProfile plots

Marginal Means of % increase in Utilization

POLICY

ContinuCjacksonC4C2C1-PMC1-AM

Es

timat

ed M

argi

nal M

eans

18

16

14

12

10

8

6

4

2

0

workload lev el

High

Low

Mod

Marginal Means of % increase in utilization

POLICY


20

18

16

14

12

10

8

6

4

2

0

Task Complexity

75% simple

0% simple

100% simple

25% simple

50% simple

Effect of Policy x Workload Level on % increase in Utilization

Effect of Policy x Task Complexity on % increase in Utilization

.



No. of Interruptions per 100 simple tasks

POLICY


Mod

erat

e w

orkl

oad

leve

l

40

35

30

25

20

15

10

5

0

No. of Interruptions per 100 simple tasks

POLICY


75%

sim

ple

task

s

50

45

40

35

30

25

20

15

10

5

0

Effect of Policy x Workload Level on # of interruptions per simple task.

Effect of Policy x Task Complexity on # of interruptions per simple task.



Mean completion time f or simple task

0

20

40

60

80

100

120

140

C1-AM C1-P M C2 C4 Cjackson Continuous

P ol i c y

Mean r esponse time f or emai ls

0

10

20

30

40

50

60

70

80

90

C1-AM C1-P M C2 C4 Cjackson Continuous

P ol i c y

Effect of Policy on mean completion time of simple tasks

Effect of Policy on mean response time of emails


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 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


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%


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


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


ResultsResults

(a) Percent Increase in Utilization

% Increase in Utilization (base value 0.9)

POLICY

CC8C4C2C1

Est

ima

ted

Ma

rgin

al M

ea

ns

16

14

12

10

8

6

4

2

0

Email Dependency

high

low

very high

very low

% Increase in Utilization (base value 0.9)

POLICY

CC8C4C2C1

Est

imat

ed M

argi

nal M

eans

16

14

12

10

8

6

4

2

Email Arriv. pattern

Expo

NonStationary Expo


ResultsResults

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

Additional Time Spent / day due to interruption

POLICY

CC8C4C2C1

Est

ima

ted

Ma

rgin

al M

ea

ns

70

60

50

40

30

20

10

0

Email dependency

high

low

very high

very low

Additional Time Spent / day due to interruption

POLICY

CC8C4C2C1

Est

imat

ed M

argi

nal M

eans

70

60

50

40

30

20

10

Email Arriv. Pattern

Expo

NonStationary Expo

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

Email Processing Tim

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

Email Processing Tim

large

Small


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


Practical SignificancePractical Significance

Use of C2 or C4 policy saves approx. Use of C2 or C4 policy saves approx. 17min/day per knowledge worker = 3 to 4%17min/day per knowledge worker = 3 to 4%

Total overhead per year with C2 or C4 policy Total overhead per year with C2 or C4 policy for a mid size organization having 100 KW for a mid size organization having 100 KW earning average salary of 5,000$ = ???earning average salary of 5,000$ = ???


Limitations of the modelLimitations of the model

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

from 1 to 5pm. Need for relaxing the work schedule!from 1 to 5pm. Need for relaxing the work schedule! 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 an interruptible primary task. In reality, KW is working on an interruptible primary task. In reality,

not all primary tasks are interruptible. For e.g. group not all primary tasks are interruptible. For e.g. group meetingsmeetings

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.


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 for e.g. blackberries.along with email for e.g. blackberries.

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

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By Ashish Gupta Ramesh Sharda Robert Greve Manjunath Kamath Mohanraj Chinnaswamy How often should we...

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