QA Staffing Model Action Research Page 1
Running head: QUALITY ASSURANCE STAFFING MODEL
Quality Assurance Staffing Model Action Research Project
Danielle Beck
May 16, 2009
QA Staffing Model Action Research Page 2
Introduction and Problem Statement
Goal
The research goal was to collect data on how Quality Assurance analysts spend
their time, analyze the data using a staffing model and present that to my director for
review and evaluation.
Introduction
I am currently the supervisor of training and quality assurance for Janus Capital
Group in the Fund Services department. Janus is a local mutual fund company that started
as a small growth shop back in 1969. The Fund Services department is the transfer agent
for the company. As the transfer agent we are responsible for taking phone calls and
instructions from our direct shareholders. Direct shareholders invest in our mutual funds
directly through us without going through an intermediary like a broker or dealer or
supermarket such as Fidelity.
Part of my current team is the Quality Assurance team. This team’s main function is to
randomly listen to phone representatives calls and listen for items that management has
deemed important. As in the call center, the QA group has also gone through many
incarnations. Today we consist of six analysts.
Problem Statement
The number of phone agents has decreased over the past few years, while the
number of QA Analysts has remained the same. Years ago a staffing model was
developed to determine if the department was right-staffed. By tracking how many calls
were assessed per hour, how many hours were spent in meetings and doing project work
(shrinkage), and how many hours were spent on vacation time management could
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determine how many units (calls assessed) were paid per hour. Management views these
criteria as a critical for determining if the team is right staffed.
The problem was that the staffing model had not been updated in a couple of
years and the manner of assessing phone calls was drastically changed last June to be
much more subjective. By researching our current volume of work, time spent in
meetings and consultations and amount of overtime being worked, a more accurate
staffing model can be developed which will determine if we are right-staffed.
Background
The Fund Services department has gone through many incarnations. When I
started as a phone representative 10 years ago I was a part of a department that, at the
height of the technology bubble in 2000, was over 1,000 employees. Over half of those
employees were phone representatives like myself. When there were over 500 phone
representatives, there were dozens of QA Analysts who listened to calls all day.
Due to fluctuations in the economy, we have less than 150 employees in the department
and about 50 phone representatives. However, we must still perform the job of handling
phone calls from our direct shareholders and helping them with their investment
decisions.
Since taking over the department in October after a company layoff I was asked
by management to reevaluate the staffing model to make sure it is still relevant. The
staffing model provides a method for management to determine if we have enough or too
many analysts. Without an accurate picture of the volume of work and the shrinkage
(meetings, reports and other assigned work) involved there is no way to determine how
productive we are as a department.
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In June the way we assess phone calls was changed to a much more subjective
method and appears to be much more time consuming to score calls. The call assessment
change was designed to align us more with our third party vendor, Prime Performance,
who surveys our customers to determine their overall satisfaction with Janus and our
representatives.
In these tough economic times it is crucial that we show we are right staffed.
However, if there needs to be another layoff an accurate staffing model may show where
cuts in QA could be made if necessary.
Research Questions
The purpose for this action research project was to create an accurate staffing
model to provide management with data on how many analysts are needed to be rightstaffed.
Question 1: How many calls does an analyst assess per hour?
Knowing this figure helped me determine if my assumption that it takes longer to
assess a call now than it did a year ago (since we changed the way we assess calls) was
valid.
Question 2: What is the average number of hours a month spent on other things such as
meetings, project work and vacation time (shrinkage)?
This helped me to see where we are spending the rest of our time outside of
listening to calls. Is this taking up too much of our time and where can we reduce it?
Question 3: How much overtime a month is the team working?
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If the number of phone representatives has decreased and there is less work, but it
takes longer to assess a call, what is a reasonable amount of overtime? How can we
reduce it?
Question 4: Will the original findings change considerably after having the analysts track
their time a month or two later?
I had the analysts track their time again the first week of April. This time of year
we use more temporary staff (which the analysts must assess their calls as well) and
because it is tax season Analysts themselves were called to the phone to help answer calls
from customers (what we call reserves). This might have had a dramatic effect on the
staffing model, however in the end it did not. Although, I still think it was beneficial to
compare these two time frames.
My thesis is based on the data that I uncovered. I believed that based on how long
it now takes to assess a phone call and with additional responsibilities and shrinkage that
the Quality Assurance group’s staffing model will show that the team is right staffed.
Methods Section
My method for this project was quantitative analysis. I had a dual role as the supervisor
of the group and the researcher collecting data and creating an accurate staffing model.
Participants
The participants of this action research project included the six QA Analysts,
myself and my director. The analysts provided me with most of the data by filling out the
Access database where they tracked their time. I analyzed the data and shared it with my
director who may make decisions about our staffing levels in the future.
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Data Collection Procedure
Data collection began by having each QA Analysts fill out a time tracker in an
Access database. This database was created a few years ago and allows the analyst to
track their time on a daily basis. The tracker requires the Analyst to enter the hours they
spent on assessing phone calls, the number of calls they assessed and a variety of other
items that are useful in determining shrinkage (See Appendix D).
Human Resources provided a monthly report detailing how many regular hours
and overtime hours each analyst worked for the month. This helped determine where
some of the overtime hours could be reduced.
Research Question
Data Collection Method
1. How many calls does an analyst
Access database time tracker. See
assess per hour?
Appendix D for an example.
2. What is the number of hours a month
Access database time tracker. See
spent on other things such as meetings,
Appendix D for an example.
project work and vacation time
(shrinkage)?
3. How much overtime a month is the
Human Resources data. See Appendix
team working?
A.
4. Will the original findings change
Access database time tracker. See
considerably after having the analysts
Appendix D for an example.
track their time a month or two later?
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Data Analysis
Analysts filled in the time tracker for three weeks. I sifted through the data by
looking at each Analyst’s tracker and determining how many hours they spent assessing
calls, how many calls they assessed and what other shrinkage they had during these three
weeks. This provided me with some triangulation of the data. As Richard Sagor states in
his book “Guiding School Involvement with Action Research,” “. . . it [triangulation]
involves the use of multiple independent sources of data to establish the truth and
accuracy of a claim.” (Sagor, 2000) Since I had all six Analysts fill out the tracker, I did
not rely on just one Analyst’s time tracking, but all of them. I was then able to compare
each of their time to see where there may be gaps in workload between the Analysts’s.
By reviewing and comparing each Analyst’s tracker I was able to see where we
spend our time and if there are any trends. I also found out that I was right in thinking
that it takes longer to review calls today than it did before we changed the way we assess
calls. Based on the data the Analysts are reviewing one less call an hour than previously.
After having gone through everything, I added everything up, reviewed the
vacation calendar, reviewed the meeting calendar and filled in the shrinkage numbers for
January, February, and March. I took the findings to management for their approval of
my methods. Management approved of the basis for my new staffing model and my
shrinkage assumptions. I also had the Analysts fill out the tracker again the first week of
April to validate my original findings on how long it takes to assess a call. This relates to
Sagor’s definition of validity, “. . . the essential truthfulness of a piece of data.” (Sagor,
200) Going forward, I will not have the Analysts fill them out on a regular basis because I
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see that as micro-management. However, it will be something I will ask the Analysts to
do periodically to make sure that we are continuing to stay right staffed.
The data that has been collected over the last few months will be summarized in
an Excel spreadsheet. The spreadsheet will be titled QA Staffing Model 2009. An
example of the spreadsheet can be seen in Appendix A - C. The spreadsheet has multiple
tabs: one tab for the Shrinkage, one tab for the Bottom Line and one tab for the Units
Paid.
The Shrinkage tab takes the data from the QA Analysts Access database time
tracker and puts into a table. Each of the six Analysts filled out a time tracker for a total
of four weeks (the last three weeks of January and the first week of April). An example of
the daily time tracker is located in Appendix D. All of their time not spent assessing
phone calls was categorized and added up into the Shrinkage tab. These amounts link to
the Bottom Line part of the spreadsheet and the Shrinkage tab.
The Bottom Line tab takes the information from the Shrinkage tab and puts it into
categories that examine how many Analysts it takes to assess back office processing
work and most importantly, how many Analysts it takes to assess phone calls. This
information is crucial to determine is we are right staffed. Also, this information flows
into the Units Paid tab.
The Units Paid tab is designed to be a brief snapshot for management so they can
view how many Analysts are required each month to do the job. Management tends to
prefer small snapshots rather than viewing all the tabs that contain a lot of information to
sift through.
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Summary
The data that was analyzed was the Access database time trackers from each of
the Analysts (see Appendix D for an example). The first time that was tracked was the
last three weeks of January. Each Analyst filled out the time tracker for each day showing
how they spent their time during the day. Items that were specifically looked at were
hours spent assessing phone calls and how many calls were assessed and how much time
was spent in meetings.
The time spent in meetings was analyzed very closely. This chunk of time goes
directly to the Shrinkage tab and as well as other items such as consultations and running
reports. These figures contribute to the overall number of Analysts that are needed each
month to assess phone calls – which is what management is closely looking at.
To report the findings the spreadsheet will be used to show the shrinkage, the
bottom line and the units paid. A table will also be used to show how the data collected
answered the proposed research questions.
A report to management will be submitted that will show an accurate staffing
model complete with shrinkage, number of calls assessed per hour and units paid.
However, this report should also include proposals and suggestions for utilizing the QA
Analysts in different ways, such as reserving in other areas and using them for special
project assignments.
Schedule
1. Have analysts fill out time tracker -- 1/5/09-1/23/09
2. Review data from trackers -- 1/26/09-2/6/09
3. Wait on HR for regular and OT hours for January – 2/9/09-2/23/09
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4. Simplify staffing model – 2/9/09-2/16/09
5. Fill in simplified staffing model – 2/16/09-2/19/09
6. Review findings with management – 2/23/09-2/24/09
7. Review with QA team – 2/26/09 -2/27/09
8. Have QA team fill out tracker for another week – March/early April
9. Wait on HR for regular and OT hours for February – 3/16/09-3/23/09
10. Fill in staffing model for February and March – 2/23/09-4/27/09
11. Final review with management – 4/27/09-4/30/09
Environmental Impact
The environmental impact of this research had the potential to be very positive
very negative. The data showed that we are right staffed, so it helps “prove” our
existence. However, if it had showed that we were over staffed, there may have been a
need to realign our resources which could have affected the team negatively by lowering
morale.
Also, if the findings had been negative, management may have asked some tough
questions of me and my group: if there needs to be another layoff, who will be let go or
how can we realign responsibilities, can someone go to part-time work? These are just
some of the tough questions I still might have to face in the future. Currently, I’m not
really sure how I will answer these.
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Literature Review
Literature Search Methods
My literature review search methods started out promising. I spoke with the head
of our scheduling department in the call center. He lent me a book about managing a call
center’s phone volumes. He thought could give me an idea about staffing models, which
it did. However, it did not help me understand the staffing model I was working with.
I turned to the Denver Public Library online and the University’s Library as well.
I just couldn’t find anything that remotely had to do with my project. I then tried Google.
I used search terms that ranged from “staffing models” to “quality assurance, call center,
staffing models.” I tried different configurations of those words and unfortunately I didn’t
have much success. I also tried using Google Scholar and Yahoo Answers. I had some
minor success with Google Scholar, but again nothing really pertained to what I was
doing for my action research. Several articles I did find were from consulting companies
that provide staffing models for a fee. They did offer some free white papers on the
subject that I will reference.
Literature Review Findings
The literature I did find was mostly about staffing call centers, call center
workers, and one article from a completely unrelated field - transportation. There is also
one article on quality assurance program, but not about staffing the QA program.
Quality Assurance Program and Cross-Training. “Getting More from Your Quality
Assurance Program” (ICMI, retrieved 2009) is an article by the International Customer
Management Institute and it details how a call center can get more out of their QA
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program. The most important idea that I took away from this article was the benefits of
having an effective quality assurance program. The article describes three main benefits:

It enables you to leverage recorded calls to identify opportunities for service
improvement within the centers, as well as organizationwide improvements.

It offers an excellent way to identify customer needs and potential long-term
service issues.

It provides another way for the call center to show the value it provides to the
organization.
When I was reading this I realized that if this is how other companies use their quality
assurance program, we are definitely not using our Analysts to their full potential. As a
management directive, our QA Analysts listen to calls and assess them based on what
management has deemed important. The QA group at Janus really doesn’t do much of
anything else. The Analysts do run certain reports for management in the call assessment
database, but it does appear based on this article, that there is a lot more we could be
doing to add value to the call center and again help “prove” our existence.
Phil Taylor and Peter Bain describe in their paper, “An assembly line in the head:
work and employee relations in the call centre,” the overall pros and cons of call center
workers in the United Kingdom. Although the article is dated (1999), a lot of what the
authors discuss and the mentality of call center workers, still holds true today. “’An
assembly-line in the head’, always feeling under pressure and constantly aware that the
completion of one task is immediately followed by another.” (Taylor & Bain, 1999) This
statement is a perfect way to describe how a call center worker feels in a queue
environment.
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When I was reading this article I kept thinking of how I could relate it to the QA
Analysts. The more I thought about it I realized that even though they do not take calls all
day every day, they do listen to calls all day every day and that same mentality can be
found in the Analysts. By looking for other ways that the QA group can add value to the
department this may help reduce this feeling of ‘an assembly-line of the head.’
Although this article didn’t have to do with staffing models, it did give me insight into
how we could add more value to the department. Another way we can add value is to
cross-train the Analysts into other area.
Professors at the University of Rochester, Edieal J. Pinker and Robert A.
Shumsky, looked to uncover the math behind training workers in multiple areas in their
paper, “The Efficiency-Quality Trade-Off of Cross-Trained Workers.” They believe that
cross-training workers can lead to economies of scale, but on the down-side it can also
lead to a loss in quality. At Janus the QA Analysts not only do their job, listen to phone
calls by full-time phone representatives, but they also have to help out on the phones
when the phones get busy. We call this reserving. Pinker and Shumsky state, “. . . the
optimal staff mix combines flexible and specialized workers.” (Pinker & Shumsky, 2000)
However, the other part of the Analyst’s job is to review and assess back-office
processing work and the Analysts do not currently reserve in the back-office. Recently
the manager of the back-office has requested a couple of the QA Analysts who primarily
deal with back-office work to reserve in the processing areas to promote cross-training
efforts department wide. The question I ask myself is, “How will this impact their overall
productivity?”
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“In this paper we study the trade-off between the cost efficiency provided by
cross-trained or generalist workers and the experience-based quality provided by
specialists.” (Pinker & Shumsky) The question still remains, “How is the QA Analyst
efficiency/productivity impacted when they have to stop listening to calls to actually take
calls or reserve in the back office?” I am hoping to answer this the first two weeks of
April. During this time, right before tax day (April 15th), the call volumes normally
increase to a point that we have to have the Analysts jump on the phone to help out with
queues. I plan to have the Analysts track their time again the first week of April to see the
impact on calls assessed per hour when they also have to jump on the phone. This will
help me with creating a more accurate staffing model.
Staffing Models. “Call Center Management on Fast Forward,” is the book on staffing
models for call centers. I borrowed this book from the head of our scheduling department.
Although I did not read the entire book, I focused my efforts on searching for specifics on
staffing models. The biggest thing I got out of this book is that staffing levels in a call
center are an art and a science. “Forecasting is the proverbial mix of art and science. It
begins with predicting how many contacts you are going to get in a future period, usually
a year.” (Cleveland, 2006) The book goes on how to try and predict contacts and looking
at historical data.
This book gave me great insight into how our call center works. How we predict
or try and forecast volumes and the appropriate staffing levels and why the scheduling
department does what they do. I tried to keep my group in mind and how could use what
I was reading to better interpret the data I was receiving.
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However, I did find some very useful information in the white paper, “Basic
Average Staffing Level Model,” by a consulting group (Tyche Consulting Group) that,
for a fee, will help design and implement a staffing model. The paper describes a model
that I think I can base mine on – gross staffing model. Although the gross staffing model
is used to determine staffing levels for a call center, I think I might be able to adapt some
of the basic concepts. “There are two basic data elements required to determine
appropriate gross staffing levels: hours of direct labor required to meet the anticipated
call load, and hours of direct labor available from the existing or planned staff.”
(Murtagh, retrieved 2009)
Thinking about my group, I need to determine the hours of direct labor required to
meet the anticipated call assessment load and the hours of direct labor available from the
existing staff. I believe the tracking of how the Analysts spend their time will lead me to
the direct hours of labor available to assess all the calls that management requires. Then
considering the existing staff level (six analysts) I can therefore look at the shrinkage on a
monthly basis which should give me my staffing model. This article was very useful to
me because I was finally able to put a name, “gross staffing model,” to what I have been
researching and collecting data on this whole time.
There are several different kinds of staffing models for call centers. The article
“What are the various methods of determining an appropriate staffing level, again by a
consulting firm, details out three different staffing models: Staffing Ratios, Gross
Staffing Models and Erlang Staffing Models. Since the Murtagh article detailed out the
gross staffing model, I focused my reading about the Erlang Model. “The Erlang model is
based on the actual incoming traffic (calls) per 15 or 30 – minute increments. This model
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can be used for support organization that have multiple shifts and assists in determining
how many professionals are needed for any given 15 or 30-minute time period.” (STI,
retrieved 2009)
I thought their description of the Erlang model sounded familiar, so I went to go
talk to my scheduling department. Not to my surprise, they explained to me that this is
the model we use in our call center. Although this article didn’t help me with my research
per se it did help me understand the staffing decisions that we make in our call center. By
understanding the decisions on the staffing model I can then make decisions about how
best to use the Analysts when they are called to the phone as reserves.
Other Staffing Models. Lastly, I found several articles from industries outside call
centers. Many of them were in reference to hospital and nursing staffing. I was not able to
relate them to my research. However, there was an article by the Minnesota Department
of Transportation (MnDOT) that detailed out their new approach to staffing models. They
spent a lot of time and money to come up with a better staffing model for all of their
different workers who work on multiple projects. “The Strategic Staffing Model Plan,
which is based on the premise that the future is impossible to predict with any accuracy,
but that the Department can help managers plan by making reasonable assumptions about
the future.” (U.S. Department of Transportation, 2002) The article goes on to describe
how external and internal scans for strengths, weaknesses, opportunities and threats
(SWOT) helped them develop their staffing models. Tony Cairns, a Management
Analyst, states “The internal scanning process identifies how employees do something,
how many employees it takes to do it, and the resources available to support it. The
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process helps focus on the high-priority work and the skill levels necessary to complete
it.” (USDT)
This is the only article I found that had nothing to do with call centers, but that I
could still see use in. The discussion around external and internal scanning and looking
for SWOT areas was very thought provoking. It made me think about my department and
what are our strengths, weaknesses, opportunities and threats. I feel that having the
Analysts track their time can help me identify those areas. I will also look to management
to see what their thoughts are on our SWOT areas.
Summary
There is a lot of information out there around call center employees and their
staffing. However, as it pertains to a staffing model around a Quality Assurance team
within a call center there is nothing. A complete void from what I can tell. Really, there
is nothing that specifically pertains to any of the research questions presented. I was able
to get some information around the value of a good Quality Assurance team and how, as
the supervisor, I should be aware of the mentality and behaviors of call center
representatives and my Analysts.
The various staffing models used within call centers, such as the gross staffing
model and the Erlang staffing model do provide great insight into how call centers staff
their phone representatives, they do not specifically address my action research.
However, by adapting the gross staffing model I was able to derive some very useful
information that I should be looking for in my data.
The other staffing model I reviewed from the Minnesota Department of
Transportation was also very helpful in thinking about SWOT.
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My action research project to come up with a viable staffing model for a Quality
Assurance team seems to be a new concept. Although, I realize that staffing models in
general are not new to fields outside call centers, it does seem that my research will fill
this void. By looking at the Quality Assurance team as a completely separate unit from
the call center to create my staffing model I see where my research may be beneficial to
others.
Findings
As a review, the table below highlights my Research Questions and Analysis. It also
contains a column on the findings for each research question posed earlier.
Research Question
Analysis
Findings
How many calls does an
This will help me determine if
According to the data
analyst assess per hour?
my assumption that it takes
collected on the Analysts
longer to assess a call now than
weekly tracker the average
it did a year ago (since we
number of calls assessed per
changed the way we assess
hour is 2. This is much
calls) is valid.
lower than before we
changed the scoring method
last June. Back then it was
over 3.5 calls assessed per
hour. See Appendix B.
What is the average
This will help me see where we As the spreadsheet in
number of hours a
are spending the rest of our
Appendix A indicates
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month spent on other
time outside of listening to
things such as meetings,
calls. Is this taking up too much 113.5 and 231 hours per
project work and
of our time and where can we
month (for the first three
vacation time
reduce it?
months of 2009) on other
(shrinkage)?
Analysts spend a total of
items including vacation
time. This number indicates
that the Analysts use a lot of
time on other items other
than assessing phone calls,
which may mean we need
to look at ways of reducing
the shrinkage.
How much overtime a
If the number of phone
The number of overtime
month is the team
representatives has decreased
hours that were recorded for
working?
and there is less work, but it
the first three months of
takes longer to assess a call,
2009 are: Jan – 96 hrs, Feb
what is a reasonable amount of
– 22 hrs and Mar – 48.
overtime? How can we reduce
These OT hours should
it?
decrease as we no longer
have temporary phone
agents. See Appendix A.
Will the original
I will have the analysts track
After having the Analysts
findings change
their time again around
again track their time for
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considerably after
March/April. This is a time of
the first week of April, the
having the analysts track
year where we use more
average number of calls
their time a month or
temporary staff (which the
assessed per hour stayed the
two later?
analysts must assess their calls
same at 2 calls assessed per
as well) and because it is tax
hour. This indicates that the
season analysts themselves
original data the Analysts
may get called to the phone to
provided was accurate.
help answer calls from
customers (what we call
reserves). This could have a
dramatic effect on the staffing
model and I think it would be
beneficial to compare this two
time frames.
Implications for Practice
The conclusions that have been drawn based on the data show that the QA team is
right-staffed currently. Although, it does also indicate that if the number of phone
representatives continues to decline, the team may find itself in an over-staffed position
and therefore will need to find alternative ways to add value to the department.
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I think that this is the biggest thing that I overlooked initially when I started this
process. At the time we had 55 full time phone representatives and over 20 temporary
phone representatives which meant a lot of phone calls to assess. However, over the
course of the first few months of this year, the number of temporary phone
representatives has dwindled and currently is at zero. We have also lost a few full-time
representatives as well.
I believe the data that the Analysts have provided is accurate and valid. However,
I have nothing else to compare it to and that can lead management to question the
reliability and validity of the staffing model. One manager has already suggested that I
have the Analysts set aside a few hours, all at the same time, where they do nothing but
assess phone calls to see if my data still holds up. I believe I could do this, but I don’t
know how reliable that data would be, since during the normal course of business this
does not happen. The Analysts are constantly interrupted by questions from supervisors,
managers, and representatives. This does not include all the meetings they attend and all
the reports they run. How can having the Analysts take time to “just assess” calls be
valid?
The literature review I performed shows that my research will fill a very specific
gap. There is a lot of information, data and even formulas on call center staffing models,
but nothing that I could find on staffing models for a quality assurance group or even a
group within a call center where their workflow is based on the staffing levels of the call
center.
Although I discussed possible tough questions if the findings were negative, I
truly believe that given the volume of work and the amount of shrinkage, we are right
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staffed. However, I may be asked to lower the amount of shrinkage, possibly by trying to
remove some meetings or use current meetings for multiple purposes and also to cut
down on the amount of overtime in the department. And at the same time find a way to
assess more calls per hour.
This action research project has added to my professional development by helping
me understand how my people are spending their time and I will be able to make the case
(hopefully) for the current level of staffing to management.
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References
Cleveland, B. (2006). Call Center Management on Fast Forward: Succeeding in today’s
dynamic customer contact environment. Annapolis: ICMI Press.
ICMI. (n.d.). Getting more from your Quality Assurance program. Retrieved March 23,
2009, from www.icmi.com.
Murtagh, S. (n.d.). Basic average staffing level model. Tyche Consulting Group.
Retrieved March 21, 2009 from
http://www.tycheconsulting.com/pdf/New/Basic%20Staffing%20Model.pdf
Pinker, E. & Shumsky, R. (n.d.). The efficiency-quality trade-off of cross-trained
workers. Retrieved April 1, 2009 from Google Scholar:
http://66.102.1.104/scholar?hl=en&lr=&q=cache:AaUqzjZfJX0J:ecow.engr.wisc.edu/cgibin/get/ie/691/2nembhard/researchpa/shumsky-msom.pdf+efficiency,+quality,+trade-off
Sagor, R. (2000). Guiding school improvement with action research. Alexandria: ASCD.
STI Knowledge Enterprise Support Solutions. (n.d.). What are the various methods of
determining an appropriate staffing level? Retrieved April 1, 2009 from
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Appendix
Appendix A – Shrinkage Tab
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Appendix B – Bottom Line Tab
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Appendix C Units Paid Tab
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Appendix D