by ashish gupta ramesh sharda robert greve manjunath kamath mohanraj chinnaswamy how often should we...
TRANSCRIPT
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
03/02/0503/02/05 2005 Big XII IS Symposium- OU 2005 Big XII IS Symposium- OU 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?
03/02/0503/02/05 2005 Big XII IS Symposium- OU 2005 Big XII IS Symposium- OU 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.
03/02/0503/02/05 2005 Big XII IS Symposium- OU 2005 Big XII IS Symposium- OU 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)
03/02/0503/02/05 2005 Big XII IS Symposium- OU 2005 Big XII IS Symposium- OU 66
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)
03/02/0503/02/05 2005 Big XII IS Symposium- OU 2005 Big XII IS Symposium- OU 77
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
03/02/0503/02/05 2005 Big XII IS Symposium- OU 2005 Big XII IS Symposium- OU 99
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?
03/02/0503/02/05 2005 Big XII IS Symposium- OU 2005 Big XII IS Symposium- OU 1010
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.
03/02/0503/02/05 2005 Big XII IS Symposium- OU 2005 Big XII IS Symposium- OU 1111
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.”
03/02/0503/02/05 2005 Big XII IS Symposium- OU 2005 Big XII IS Symposium- OU 1212
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
03/02/0503/02/05 2005 Big XII IS Symposium- OU 2005 Big XII IS Symposium- OU 1313
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.
03/02/0503/02/05 2005 Big XII IS Symposium- OU 2005 Big XII IS Symposium- OU 1414
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
ContinuCjacksonC4C2C1-PMC1-AM
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
.
03/02/0503/02/05 2005 Big XII IS Symposium- OU 2005 Big XII IS Symposium- OU 1515
P-I Result analysis cont… P-I Result analysis cont… Profile plotsProfile plots
No. of Interruptions per 100 simple tasks
POLICY
ContinuCjacksonC4C2C1-PMC1-AM
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
ContinuCjacksonC4C2C1-PMC1-AM
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.
03/02/0503/02/05 2005 Big XII IS Symposium- OU 2005 Big XII IS Symposium- OU 1616
P-I Result analysis cont… P-I Result analysis cont… Profile plotsProfile plots
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
03/02/0503/02/05 2005 Big XII IS Symposium- OU 2005 Big XII IS Symposium- OU 1717
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
03/02/0503/02/05 2005 Big XII IS Symposium- OU 2005 Big XII IS Symposium- OU 1818
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%
03/02/0503/02/05 2005 Big XII IS Symposium- OU 2005 Big XII IS Symposium- OU 1919
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
03/02/0503/02/05 2005 Big XII IS Symposium- OU 2005 Big XII IS Symposium- OU 2020
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
03/02/0503/02/05 2005 Big XII IS Symposium- OU 2005 Big XII IS Symposium- OU 2121
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
03/02/0503/02/05 2005 Big XII IS Symposium- OU 2005 Big XII IS Symposium- OU 2222
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
Email Arriv. Pattern
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
Email Arriv. Pattern
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
03/02/0503/02/05 2005 Big XII IS Symposium- OU 2005 Big XII IS Symposium- OU 2525
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
03/02/0503/02/05 2005 Big XII IS Symposium- OU 2005 Big XII IS Symposium- OU 2626
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$ = ???
03/02/0503/02/05 2005 Big XII IS Symposium- OU 2005 Big XII IS Symposium- OU 2727
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.
03/02/0503/02/05 2005 Big XII IS Symposium- OU 2005 Big XII IS Symposium- OU 2828
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????