time allocation - a measurement tool of productivity in the workplace
TRANSCRIPT
TIME ALLOCATION:
A MEASUREMENT TOOL OF PRODUCTIVITY IN THE WORKPLACE
A Thesis
Presented to
The Faculty of the Department of Psychology
San José State University
In Partial Fulfillment
of the Requirements for the Degree
Master of Science
by
Trevor Emery Olsen
August 2010
UMI Number: 1482574
All rights reserved
INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted.
In the unlikely event that the author did not send a complete manuscript
and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion.
UMI 1482574
Copyright 2010 by ProQuest LLC. All rights reserved. This edition of the work is protected against
unauthorized copying under Title 17, United States Code.
ProQuest LLC 789 East Eisenhower Parkway
P.O. Box 1346 Ann Arbor, MI 48106-1346
© 2010
Trevor Emery Olsen
ALL RIGHTS RESERVED
The Designated Thesis Committee Approves the Thesis Titled
TIME ALLOCATION: A MEASUREMENT TOOL OF PRODUCTIVITY IN THE WORKPLACE
by
Trevor Emery Olsen
APPROVED FOR THE DEPARTMENT OF PSYCHOLOGY
SAN JOSÉ STATE UNIVERSITY
August 2010
Dr. Howard Tokunaga Department of Psychology
Dr. Megumi Hosoda Department of Psychology
Mr. Shawn Beatty National Semiconductor Corporation
ABSTRACT
TIME ALLOCATION: A MEASUREMENT TOOL OF PRODUCTIVITY IN THE WORKPLACE
by Trevor Emery Olsen
What is productivity? The degree of productivity at work is one of the primary
measures of success or personal achievement. Productivity is also often thought of as a
resource allocation process through which energy is allocated across actions or tasks to
maximize need satisfaction. Out of this discussion of productivity is born the idea that
we can assess productivity through the use of time allocation measurement.
The present study seeks to create a unique time allocation measurement tool to
assess the overall time distribution across a set of comprehensive work task categories as
well as collect data related to the perceived criticality of specific work tasks.
Furthermore, additional analysis regarding the total number of hours worked per week
and the total number of years of work experience are also considered. After discussing
the implications of the time allocation distribution results, the findings are then connected
back to the concept of overall productivity assessment, and a determination is made
regarding the effectiveness of utilizing a time allocation measurement tool as a valid
measure of productivity.
ACKNOWLEGEMENTS The process of completing a Master’s thesis is difficult to say the least and I could
not have done it without the help of some very special people. First, I would like to thank
my thesis chair, Dr. Howard Tokunaga, for all his wisdom, guidance, and perspective
throughout this process. Second, I would like to thank Dr. Megumi Hosoda, whose
feedback and suggestions contributed significantly to the eventual final product. Third, I
would like to thank Shawn Beatty for not only hiring me as an intern, but also
supervising the entire time allocation project and providing valuable suggestions along
the way. I could not have completed this project without all of your amazing
contributions.
I would also like to thank my parents, Emery and Royceann Olsen, for getting me
through those rough early academic years and for always believing in me. I hope I’ve
made you proud. To my awesome brother Tyler, thank you for putting up with me for so
many years when we were younger. I know I was a pain, but if it’s any consolation, I
always admired your thirst for knowledge. I really hope you find something that you’re
once again passionate about.
And now, last but certainly not least, I need to take this opportunity to thank my
biggest supporter, the woman behind the scenes who kept me going through good times
and bad, my amazing wife, Julie. I never imagined that I’d complete a Master’s thesis
and you had everything to do with it, so thank you and I love you so much.
v
TABLE OF CONTENTS
Page LIST OF TABLES.…………….......………………………………………………….....vii INTRODUCTION….……………………………………………………………………..1 METHOD...……………………………………………………………………………...23 RESULTS………..…………………………………………………………………..…..30 DISCUSSION…………………………………………………..………………………..42 REFERENCES……………………………………………………………..…..……......49 APPENDIX: TIME ALLOCATION MEASUREMENT TOOL….……………………54
vi
LIST OF TABLES Number Page Table 1: Inventory of Work Tasks and Task Descriptions Grouped by Work Task Category……..….…………………….………………………..26 Table 2: Descriptive Statistics of Years of Work Experience and Number of Hours Worked Per Week……..………………………….………….…..30 Table 3: Time Allocation Distribution Across Work Task Categories and Frequency of Critical Work Tasks…….……….………………………..33 Table 4: Time Allocation Distribution Within Work Task Categories and Frequency of Critical Work Tasks…………………………………..…..40
vii
1
Introduction
What is productivity? How is productivity measured? How can we improve
productivity? All of these questions relate back to one of the central issues of
industrial/organizational psychology research and that is the study of productivity
(Lindell, Clause, Brandt, & Landis, 1984; Pritchard, 1992; Pritchard, Harrellm,
DiazGranados, & Guzman, 2008; Quinn, 1978; Sink & Smith, 1994; Singh, Motwani, &
Kumar, 2000). It is as big and far-reaching as any construct in psychology because it can
be applied to every aspect of our daily lives (Gomar, Haas, & Morton, 2002; Koss &
Lewis, 1993; Lord, 2002; Sanchez, 2000; Sen, 1988; Souza-Poza, Schmid, & Widmer,
2001; Tuttle, 1981). For most people, how productive they are during the day or over the
course of a week, month, year, etc. is one of the primary methods by which people
measure success or personal achievement (Borman, Dorsey, & Ackerman, 1992; Gross,
1984; Minge-Klevana, 1980; Misterek, Dooley, & Anderson, 1992), which is why
productivity measurement is so important. Productivity means different things to
different people (Gomar, et al., 2002; Souza-Poza, et al., 2001; Tangen, 2002; Twedt,
1966; Vosburgh, Curtis, Wolverton, Albert, Malec, Hoben, & Liu, 1984). For example,
from the day-to-day activities of stay-at-home moms to the time allocation of software
engineers to even the hunting patterns of remote hunter-gatherer tribes, everything can be
viewed through the lens of productivity.
Productivity
When thinking about the concept of productivity, it is important to consider the
physical boundaries within which all humans function. A theory posited by Pritchard
2
(1992) states that people have a defined amount of energy, referred to as the “energy
pool,” that they can access to satisfy specific needs. At the most basic level these needs
are such things as acquiring food, water, and safety, but higher level needs are such
things as obtaining achievement, recognition, and power (Pritchard, 1992). The energy
pool varies across people and over time for any individual, so it is important to remember
that the amount of energy focused on any one specific need can change quickly
(Pritchard, 1992). The energy pool concept developed by Pritchard (1992) has many
similarities to Kanfer and Ackerman’s (1989) theory that people have a limited amount of
resources that they can apply to the completion of any one specific task. The energy pool
essentially creates an environment that rewards efficient and productive behavior and
punishes wasteful and lazy behavior because once time is spent on one activity, it cannot
be recovered and applied elsewhere.
In conceptualizing productivity as it relates to satisfying needs and best utilizing
time as a finite resource, let’s also consider Pritchard, et al. (2008) where the motivation
to satisfy needs leads to the definition of productivity as “a resource allocation process
through which energy is allocated across actions or tasks to maximize the person’s
anticipated need satisfaction.” In this case, all resource allocation is focused on the goal
of need satisfaction, which is another way of conceptualizing productivity.
However, thinking of productivity as simply the utilization of an “energy pool” to
satisfy needs is a bit too simplistic. While productivity is undoubtedly connected to the
use of available resources, it is also linked to the concept of value creation, which refers
to high productivity ultimately being achieved when actions, behaviors, and resources are
3
utilized in such a way that they ultimately improve over time. Value creation can be
thought of as the positive result of an action or behavior. Furthermore, through the
process of value creation the concept of continuous improvement is born (Tangen, 2002).
Without the element of continuous improvement being a part of the conceptualization of
productivity, we would instead be measuring levels of production rather than
productivity, which is simply the quantity of a product or service produced (Tangen,
2002). In other words, value creation is refining the process over time so that greater
output is created for the same amount of input energy.
As referenced above, productivity should not be confused with production.
Similarly, concepts such as profitability, performance, efficiency, and effectiveness also
should not, in and of themselves, be mistaken for productivity (Forrester, 1993; Gomar,
et al., 2002; Gowda & Chand, 1993; Koss, et al., 1993; Miller, 1984; Misterek, et al.,
1992; Pritchard, 1995; Thadhani, 1984). While all of these concepts may be related to
productivity on some level, none of them fully capture the dynamic, multi-dimensional
nature of productivity (Koss, et al., 1993; Forrester, 1993; Misterek, et al., 1992; Miller,
1984; Sink, et al., 1994; Singh, et al., 2000). Meaning that while each of the aspects of
productivity listed above relate to the overall concept of productivity, none of them fully
encompass the concept of productivity.
Productivity is bigger than just a single aspect previously described. It is not just
a score on a test or the number of widgets built in an hour or the amount of time spent on
the job. Instead, productivity is better thought of as the allocation of time, energy, and
resources in the most efficient manner possible. It is the cumulative effect of dozens of
4
separate actions, behaviors, and decisions. In the present study we will focus specifically
on the allocation of time because it is uniquely suited for application to the workplace,
and in no other area of our daily lives is productivity more important than in the
workplace. Yet productivity is often neglected because many organizations do not
understand what productivity actually means (Tangen, 2002).
One of the clearest applications of productivity assessment can be found in the
workplace. It is a place where people, technology, innovation, and collaboration all come
together to design, build, sell, deliver, and repair everything from laundry detergent to
silicon wafers and with all of the interconnected layers of the workplace, the challenge of
understanding productivity is of great importance. It is the balance between cost and
benefit, where every action comes at a price, whether the price is time, money,
technology, etc. In the workplace, productivity is about understanding where the greatest
need, leverage point, value add, or next breakthrough is and figuring out how to
maximize resource allocation in that area.
To take this idea a step further, productivity in the workplace is not just about
measuring the amount of time spent on a particular activity but also understanding the
value of time spent on that activity and whether that time could be better utilized on a
different activity (Tangen, 2002). Employees spend a finite amount of time at work and
the allocation of that time can mean the difference between success and failure for a
company, meaning that employees need to efficiently and effectively manage their time
and allocate their time appropriately to their designated work tasks. Similarly, companies
also have a finite production capacity at any point in time, so if an employee spends his
5
time at work on tasks and activities that only deliver mediocre results, then the whole
company is not functioning as efficiently and effectively as it needs to be in order to be
successful (Chand, et al., 1996).
How to accurately define the concept of productivity in the workplace is a
question that has been debated for decades (Ilgen & Klein, 1988; Pritchard, 1992;
Tangen, 2002; Tuttle, 1981; Sink, et al., 1994) and will probably continue to be a point of
contention for many decades to come. One perspective is that there should only be one
“standard” definition of productivity in the workplace, while another perspective is that
there are too many interconnected variables involved to limit productivity in the
workplace to a single definition (Pritchard, 1995). The inherent complexity of assessing
productivity in the workplace means that having one standard definition of productivity is
virtually impossible, and the assumption that everyone defines productivity the same way
is simply false (Quinn, 1978). Although definitions of productivity can vary greatly
depending on the context and environment, most definitions fall into one of three
generally accepted categories: the Economist, the Engineer, and the Manager (Pritchard,
1995; Quinn, 1978). Under the Economist definition, productivity in the workplace is
thought of as a pure ratio of outputs over inputs and is considered strictly a measure of
efficiency. With the Economist definition, it is all about the efficient utilization of time
and resources (Pritchard, 1995; Quinn, 1978). The next definition of productivity is the
Engineer definition, which adds another level of complexity to the basic assessment of
efficiency (outputs/inputs), an evaluation of effectiveness of output (output/goals). With
the addition of another layer of complexity, this definition of productivity now includes a
6
reference point to the overall objectives and/or goals of the organization and introduces
the concept of value into the discussion of productivity in the workplace (Pritchard, 1995;
Quinn, 1978). This is similar to the previously described concept of value creation. The
third definition of productivity is the Manager definition, which expands the reach of
productivity beyond just efficiency and effectiveness to include any action or behavior on
the part of the employee that causes the organization to function better (Pritchard, 1995;
Quinn, 1978). This could include such organizational constructs as innovation, talent
management, employee engagement, or creativity.
While each of these definitions of productivity in the workplace attempts to
capture the true meaning of productivity, there are still clear differences and potential
strengths and limitations with each one. Upon first examination of the Economist
definition, it appears to be the most straightforward, quantifiable, and objective definition
of productivity in the workplace, and while each of these characteristics make it desirable
for collecting and measuring data, they also limit how the data can be generalized outside
of the specific group, division, or organization where the data are collected. The
measurement of inputs and outputs, or efficiency, without context is like collecting data
in a bubble, making it impossible to determine the value creation of a given set of inputs
and outputs. Without a reference point for the measurement of productivity, the data lack
impact and generalizability to other departments within the company, other organizations
outside the company, or even against internal benchmarking data on productivity.
Seeking to address this limitation, the Engineer definition of productivity in the
workplace uses the same measure of efficiency, but then introduces the concept of
7
effectiveness, which provides the missing reference point that connects efficiency
measurement with organizational success (Pritchard, 1995; Quinn, 1978). Now instead
of productivity being limited to just a ratio of outputs to inputs, it incorporates the
measurement of effectiveness and then asks the question, “Are these specific outputs
contributing to the overall success of the organization?” Suddenly, productivity in the
workplace is not just about doing anything well, but about doing the right things well.
If you take this definition a step further, then you get the Manager definition of
productivity in the workplace, which operates under the assumption that the right things
an organization needs to do well to succeed include all manner of organizational
constructs beyond what can be quantifiably measured. While this definition of
productivity acknowledges the complex nature of organizations by including a diverse
mix of organizational variables (e.g., leadership, communication, team-building), therein
lies the limitation of such a definition (Pritchard, 1995; Quinn, 1978). The inclusion of
additional variables that are not easily measured and that are not directly associated with
organizational output can have a detrimental effect on the assessment of productivity by
distorting potentially valid findings. For example, attempting to assess productivity
through the measurement of leadership ability is very difficult because often the activities
associated with being a good leader are not quantifiable.
In the same way that the Manager definition attempts to incorporate too many
variables into the assessment of productivity, the Economist definition does not include
enough variables. The true value of the Engineer definition is that it acknowledges the
importance of assessing the measure of output within an organizational context, while
8
also maintaining the integrity of measurement by utilizing a quantifiable approach. Of
particular interest here is the commitment to a quantifiable approach of assessing
productivity. Organizations, regardless of how big or small, are all subject to the
unchanging constant of “time.” Time levels the playing field for all organizations and
provides a constant, quantifiable baseline by which data can be collected and compared.
For this reason, as well as the added benefits previously indicated, the Engineer definition
of productivity offers the best methodology for assessing productivity in the workplace
and will be utilized for the purposes of this study.
In the corporate environment of today, businesses succeed or fail based on
productivity, so being able to accurately measure productivity is of critical importance
(Bailey, 2000; Chand, Moskowitz, Novak, Rekhi, & Sorger, 1996; Forrester, 1993;
Pritchard, et al., 2008). Assessing the productiveness of an organization’s IT system,
supply chain, distribution network, and marketing strategy are all vital to the success of
an organization, but perhaps the single most important measure of an organization’s
success is of its workforce. More specifically, this refers to how employees choose to
allocate their time over the course of a day, a week, a month, etc. In the workplace,
productivity measurement is much less concerned with the amount of time needed to
satisfy basic human needs, and instead is concerned with the amount of time and energy
spent working on the job. For example, understanding the day-to-day activities of the
average employee can lead to a better understanding of the overall strengths and
weaknesses of the company. This process can also help reveal gaps between a “desired”
state for the company and the current “reality” of the company.
9
Often productivity in the workplace is measured in quantifiable terms, whether it
is the number of lines of code entered by a programmer in a given month, the time
needed to complete a specific task, or the number of sales calls made in an hour (Bailey,
2000; Chu & Lin, 1993; Gowda, et al., 1993; Huy, 2001; Norman & Nunamaker, 1989).
All of these measures of productivity in the workplace focus on quantifiable data
(Thadhani, 1984; Vosburgh, et al., 1984; Twedt, 1966), and the reasons for this are all
valid. First of all, quantifiable data collection is much less susceptible to subjective
interpretation and bias on the part of the data collector. Attempting to collect qualitative
data via observational means leaves a lot of interpretation up to the researcher in terms of
what constitutes the observable trait or behavior. In response to this limitation,
quantitative data collection focuses on specific observable actions that have been
explicitly defined ahead of time. By providing clear definition of measurable variables
ahead of time, the likelihood of data collection errors is significantly reduced.
Additional advantages of quantitative data collection are that data can be collected
and compared over time and can also be more easily generalized to other business groups
and organizations. In the example of the number of sales calls made per hour, the same
data can be collected three months, six, months, or two years later, which allows for
direct comparison and analysis of changes in the data over time. This also allows for data
to be more easily generalized to other business units within the same organization or
other companies that utilize a sales force because all employees of this industry function
under same time constraints during the workday. Both advantages of quantitative
10
measurement described above are critical to the long-term goal of productivity
assessment.
But just as the Economist definition of productivity too narrowly focuses on a
strictly quantitative approach, there is more to productivity measurement than just the
numbers. It is also critical to factor in variables that provide context for the data
(Borman, et al., 1992; Dierdorff & Wilson, 2003; Gross, 1984; Lindell, et al., 1998). To
keep with the same example of sales calls per hour, the quantitative data only makes
sense within the context of how many sales calls are expected to be made in a hour and/or
what other responsibilities might prohibit a sales representative from reaching the goal of
sales calls per hour. Given the need to collect both quantitative and contextual data
simultaneously, what approach would best facilitate this type of data collection? As
previously mentioned, the assessment of productivity through the use of time
measurement is a popular technique (Borman, et al., 1992; Dierdorff, et al., 2003; Gross,
1984; Lindell, et al., 1998; Miller, 1986), but does it meet all the necessary requirements
for both types of data collection?
Time Allocation
From the time allocation measurement of stay-at-home moms to the time
allocation of software engineers, measurement of time utilization through the use of time
allocation techniques has been applied to many different areas of study (Gross, 1984;
Schmidt & DeShon, 2007; Sink & Tuttle, 1989; Singh, et al., 2000; Souza-Poza, et al.,
2001). One of the main reasons that time measurement is such a useful data collection
technique is that observable behaviors can be measured within the framework of unitized
11
time, which means they can be measured in seconds, hours, days, etc. Measuring
behavior in this manner is beneficial because every action has an observable beginning
and end. In practical application, this is very different from trying to assess an
individual’s thoughts, attitudes, and/or intentions, which may or may not motivate them
to engage in the displayed behavior (Gross, 1984) and can be much more difficult to
assess using traditional data collection methods. Quantitative data, collected via time
measurement techniques, focus on observable behavior by looking at the amount of time
allocated to specific actions. Time allocation data of this kind is relevant to the
assessment of productivity because productivity is essentially the efficient use of unitized
time.
Another benefit of utilizing time measurement techniques in the assessment of
productivity is that all humans operate under the basic assumption that when a person is
doing one thing, he is restricted as to what else he can do simultaneously (Chu, et al.,
1993; Gross, 1984; Huy, 2001). This is often referred to as “time budgeting” and it
allows time measurement techniques to more easily differentiate between separate
measurable behaviors (Gross, 1984). As a result of being able to distinguish between
these behaviors, it becomes much easier for the researcher to identify which behaviors are
most prevalent and potentially most significant to the assessment of productivity (Gross,
1984). Without being able to differentiate between specific behaviors, the ultimate goal
of relating time allocation back to productivity would be impossible, and in no other
environment is this more critical than in the workplace (Bailey, 2000; Borman et al.,
1992; Koss, 1993; Miller, 1984).
12
One of the main advantages of using the assessment of time allocation as a
measurement technique of productivity in the workplace is that it has the capability of
measuring individual employee-level behaviors (Pinsonneault & Rivard, 1998; Pritchard,
1995; Tangen, 2002). In other words, time allocation provides a measure of productivity
specific to the job role by focusing on the unique tasks and responsibilities of each
individual employee. By concentrating on the time allocation of employees at the
individual task level, the data collection process is simplified by the fact that each
employee can be assessed directly. Furthermore, work task data collected at the
employee level can also be aggregated and assessed at the work team-level and
organization-level. This is due to the standardization of the measurement technique and
the fact that the job duties and responsibilities are generally the same for every employee
in the same job level.
Another benefit of utilizing time allocation measurement in the workplace is that
most office-based work environments are naturally task-oriented, which makes them
easier to assess than other environments where behaviors are much less well defined. In
the workplace, most, if not all, positions have at least a functional job description, which
typically highlights everything from specific task responsibilities to a desired skill set for
an employee. Having reference materials of this kind can make the data collection
process easier because work tasks are properly defined. When work tasks are properly
defined, the process of identifying task completion becomes much easier for the
employee and thus improves the employee’s ability to budget time appropriately to
complete the task. Having defined work tasks also eliminates ambiguity around which
13
tasks are considered most important or require the greatest amount of time and/or
resources to complete.
Since time is of such great value to an organization, any evaluation technique that
allows for an employee to quickly allocate time to the completion of work tasks that
contribute significantly to the success of the organization contributes to the goal of time
allocation measurement. If an employee is spending too much time figuring out the
process of completing a specific work task or determining whether it is critical to the
organization’s success, then the resources of the organization are not being utilized to
their full potential and overall productivity suffers (Borman, et al., 1992; Sink, et al.,
1994; Thadhani, 1984; Vosburgh, et al., 1984).
What the scenario above illustrates is that time allocation measurement techniques
must also include contextual variables in order to better understand how and why time
allocation distributions vary across individuals in the same job function. Time allocation
measurement in the workplace is about not only collecting data on the amount of time it
takes an employee to complete a task, but also understanding why it took the employee
that amount of time to complete the task. Were the requirements of the task unclear?
Was the employee unequipped with the knowledge and/or skills to complete the task?
Was there a perception on the part of the employee that the task did not add value to the
organization? These are all questions that provide context to the quantitative time
allocation data and are absolutely necessary when it comes to collecting meaningful time
allocation data.
14
Referring back to the Engineer definition of productivity, the measurement of
efficiency (quantitative data) only provides a single piece of the productivity puzzle, and
it is not until a measure of effectiveness (qualitative data) is introduced that the whole
picture begins to come into focus. The present study seeks to build upon this premise by
incorporating both qualitative and quantitative data collection in the time allocation
measurement of productivity in the workplace.
In order to accurately assess productivity in the workplace through the use of a
time allocation measurement tool, a critical step is properly defining exactly what time
allocation measurement really means. Unfortunately, the present body of time allocation
research not only lacks a common definition of time allocation measurement, but in many
cases also fails to provide a specific definition of any kind (Chand, et al., 1996; Gomar, et
al., 2002; Gross, 1984; Miller, 1986; Sen, 1988). The definition of time allocation
measurement is often assumed without any further clarification on the part of the
researcher. This is a poor assumption to make as it limits the generalizability of research
findings by leaving the definition of time allocation measurement up to interpretation. If
the purpose of time allocation measurement is properly defined, then similar time
allocation research can be more easily compared and generalized.
Let us consider an example of a study that does not explicitly define time
allocation measurement. In an article focusing on dynamic goal prioritization by Schmidt
and DeShon (2007), time allocation measurement was conceptualized as an expected
time allocation distribution, rather than explicitly defined as a concept. In this case, the
expected time allocation distribution referred to the expectation that there would be time
15
allocation shifts between tasks depending on changing environmental factors. Schmidt
and DeShon (2007) posited that if each task provided equal incentive for completion to
the participant, then participants would allocate more time to tasks that would take the
longest to complete or were perceived to be the most complex. It was also predicted that
if incentives were not equally distributed, meaning that certain tasks provided a greater
incentive to the participant if completed, then participants would shift their time
allocation toward the high-incentive tasks. Notice that the measurement of time
allocation data was not explicitly defined, but instead was conceptualized in relation to an
expected time allocation distribution.
Now let’s take a look at a research study where time allocation measurement was
clearly conceptualized and defined. In a meta-analysis of job analysis reliability by
Dierdorff and Wilson (2003), one of the key elements of standardizing job analysis data
was to assign observable work task behaviors to one of two separate categories: task-
level data or general work activities. Task-level data were defined as “…information that
targets the more microdata specificity” and general work activities were defined as
“general activity statements applicable across a range of jobs and occupations,” inspired
by a definition of general work activities originally described by Cunningham, Drewes,
and Powell (1995). By clearly defining how observable behaviors were to be measured
and categorized, the process of evaluating existing data for the meta-analysis became
much easier. While the previously described study focused primarily on job analysis
reliability, rather than time allocation measurement, it did clearly define how work tasks
would be categorized and subsequently how a participant’s time would be allocated to
16
different work tasks. Future job analysis research, as well as productivity research using
time allocation measurement tools, will continue to benefit from the definitions of time
allocation described in this study because standardization helps to increase the
generalizability of findings. Although work task behaviors observed in the study
described above were not categorized in the same way as in the present study, the fact
that work task behaviors were explicitly defined and categorized makes it relevant to the
present study.
Given the benefits of clearly defining time allocation measurement, the following
definition will be utilized for the purposes of the present study. Time allocation
measurement is defined as the collection of both quantitative time data and qualitative
value measurement data related to the allocation of time needed to complete specific
work tasks.
From this definition of time allocation comes the process of developing a valid
measurement tool of time allocation, which specifically addresses the need for both
qualitative and quantitative data collection in the workplace. Unfortunately, the existing
body of time allocation research does not offer much in the way of validated time
allocation instruments. The vast majority of time allocation research tends to be
observational in nature, which means that not only is qualitative data impossible to
collect, but also there is no standardization of the actual measurement tool because each
researcher must create a unique inventory of observable actions and/or behaviors for that
particular study (Chand, et al., 1996; Gomar, et al., 2002; Gross, 1984).
17
For example, in Gross’s (1984) study of cultural behavior, the author notes that
even in the situation where multiple researchers are observing the same behavior of a
sample population, there are often significant discrepancies between the actual behaviors
observed by the researchers, even in cases where the same behavior is observed by
multiple researchers. By focusing on strictly observable actions, a researcher loses the
ability to collect data of not only what alternative behaviors the participant could have
engaged in, but also why the participant chose to engage in a specific behavior. In
response to these challenges, the workplace provides a unique solution. Companies are
designed with standardization in mind, so very often a comprehensive list of all work
behaviors has already been created. As a result, not only can a researcher know which
behaviors are being engaged in, but also which ones are not. An observational approach
to time allocation measurement could not provide this type of data collection because the
researcher can only account for observable actions. Consequently, the time allocation
measurement tool developed for the present study overcame this obstacle and an
inventory of all work tasks was included in the time allocation measurement tool.
A study conducted by Borman, Dorsey, and Ackerman (1992) on the time
allocation of stockbrokers also emphasizes the importance of using a reliable time
allocation measurement tool. In their study, researchers found that variation in time
allocation ratings were associated with actual differences in employee performance on
several work task dimensions. For example, participants who reported spending time on
activities such as “dealing with corporate clients” and “advising/helping other
stockbrokers” correlated positively with sales performance. Their conclusion was that
18
the differences in reported time spent on specific task dimensions reflected systemic
variation in time allocation strategies between novice and more experienced
stockbrokers. In order to assess the time allocation of the stockbrokers in their study, a
measurement tool was developed, referred to as the “Job Activities Checklist,” which was
essentially a comprehensive task inventory. The final version of the checklist used in the
study included 160 non-overlapping activity items, which were all collected via
researcher observations and incumbent interviews. One of the benefits of utilizing this
type of time allocation measurement tool was that all participants were measured against
the same task inventory. By utilizing a task inventory validated by incumbents and
subject matter experts, the likelihood that observational data collected by researchers is
comprehensive and accurate is much greater. This is an aspect of observational research
that is often overlooked (Gross, 1984; Borman, et al., 1992).
Although Borman et al. (1992) identified correlations between the time spent on
specific work tasks and performance and also utilized a valid task inventory measurement
tool, it still did not assess the employee’s perceived value of each completed work task.
Consider the example of an employee who chooses not to spend time working on a
specific task. This choice may have been made for a number of different reasons (i.e.
unfamiliarity with the task, the task was part of a long-term project without a short-term
deadline, or the perception that the completion of the task did not contribute significantly
to the success of the team or organization) and the end result was that the employee did
not spend time on the specific task. The complimentary aspects of time allocation data
gathered by collecting both qualitative data and quantitative data in this example provide
19
a context and helps answer the question of why the employee did not spend time on the
specific work task (Huy, 2001). In the present study, a model of data collection is
proposed that incorporates the assessment of not only the actual time spent on a specific
work task, but also the perceived criticality of the work task. By doing so we can begin
to understand why certain tasks receive a greater percentage of time allocation, as well as
separate out work tasks that are perceived to be critical to the success of the organization
and work tasks that are not.
Another interesting component of time allocation measurement in the workplace
that has not previously been addressed specifically in the time allocation literature is the
fact that not all employees work the same amount of time over the course of a day, week,
month, year, etc. Some employees work more hours due to excessive workload
imbalance, company expectations, desire for advancement, etc. and some employees
work less hours due to a lack of work projects, lack of motivation, transition to a flexible
work schedule, etc. Taking into consideration the amount of time an employee spends on
the job is critical when evaluating the allocation of time across multiple work task
behaviors. For example, if an employee spends 10% of their workday responding to
work-related email, then depending on whether the employee is part-time and works
20/hours per week or is full-time and works 60/hours per week, anywhere between 24
minutes per day up to an hour and 12 minutes per day could be spent on the work task of
responding to work-related email. That is a potential difference in actual time spent on a
work task of 48 minutes. Accounting for this type of variation in actual work hours is
important in this example and has similar implications for the present study since
20
absolute time data in hours was collected from each participant. In response to this need,
data related to the number of hours worked per day were collected from all participants in
the study.
In summary, the existing body of productivity research in the workplace has
provided some great examples of how time allocation measurement can be used as an
assessment tool (Chand, et al., 1996; Gomar, et al., 2002; Borman, et al., 1992; Gross,
1984). However, there is still much room for improvement and the purpose of the
present study is to address a number of the current limitations in the productivity research
body and more specifically in the application of time allocation measurement techniques
in the workplace. By first clearly defining both productivity and time allocation
measurement we have established a reference point by which progress can be assessed
and subsequent research can be compared. Then through the process of creating an
original time allocation measurement tool that takes into account the amount of time
spent on the job, we can begin to assess the overall distribution of time across all work
task categories by collecting actual time allocation data related to each specific work task,
as well as the perceived criticality of each work task. One of the central limitations
identified in the time allocation literature has to do with the lack of qualitative data
collection, which is addressed in the present study by assessing a participant’s perception
of work task criticality. Perceived work task criticality relates to qualitative data
collection in that the participant is given the opportunity to provide a subjective value
judgement on the quantitative time allocation data. Beyond just looking at the literal
number of hours allocated to each work task, participants also reported which work tasks
21
were considered to be most critical to the overall success of the organization. By
collecting both qualitative (criticality) and quantitative (time) data, the process of
determining why certain work tasks receive a specific time allocation percentage
becomes easier because you know whether the task is perceived to be critical to the
overall success of the organization.
The existing body of productivity research and time allocation measurement
research is not without limitations and the purpose of the present study is to address a few
specific issues through the creation of a new time allocation measurement tool. Namely,
these limitations are the lack of subject matter expert utilization when creating a work
task inventory, the failure to account for the total number of hours worked in a given
week, and most notably the lack of qualitative data collection when assessing the overall
time allocation of a given population. Through the process of addressing the issues
outlined above, the ultimate goal of constructing a unique and valid time allocation tool
and accurately assessing productivity through the use of the time allocation tool that
collects not only quantitative time data, but also qualitative criticality data is made
possible. Productivity is an elusive concept and time allocation measurement is a way of
not only quantifying productivity by assessing the amount of time spent on specific work
tasks, but also qualifying productivity by assessing the criticality of work tasks. The
present study will first focus on the collection of an overall time allocation distribution
across all work task categories and then assess whether accounting for the number of
hours worked in a typical week influences the time allocation distribution and also
whether the perceived criticality ratings of specific tasks provides any additional
22
information or clarification as to why the time allocation percentages are the way they
are. Similar analysis regarding the criticality of work tasks will also be applied within
each separate work task category to determine whether perceived criticality is a useful
variable in the overall assessment of time allocation and productivity.
23
Method
Participants
The collection of data for this study was conducted within one of the strategic
business units of a major Silicon Valley technology company and more specifically
focused within the Applications engineering job function. A total population of 84
Applications engineers was present within the Signal Path business unit at the time of
data collection and all employees were encouraged to participate. The population
consisted of a mix of managers, senior-level, mid-level, and entry-level individual
contributors. Employees were not required to participate and no additional incentives or
rewards were provided to encourage participation. The final sample consisted of 61
engineers, resulting in an overall response rate of 73%. Given the small population size,
the collection of demographic information was kept to a minimum to preserve the
anonymity and confidentiality of participants. The mean years of experience for
employees prior to joining the current tech company was 6.55 years (SD = 8.43), ranging
from zero previous experience to 33 years. The mean years of experience for employees
at the current company was 7.22 years (SD = 6.31), ranging from 1 month to 33.50 years.
Overall, the employees’ mean years of experience was 13.77 years (SD = 9.85) for all
Applications engineers within the Signal Path business unit.
Procedure
The Applications engineers were asked to fill out a personal computer (PC)-based
electronic time allocation measurement survey consisting of several matrices related to
the different work task categories associated with the Applications job function. Prior to
24
the Applications engineers receiving the time allocation measurement tool, all employees
were required to attend one of several informational meetings designed to educate them
about the purpose of the survey. Participants were instructed that all survey responses
would be completely confidential and anonymous. The engineers were then given a
week to individually complete the time allocation measurement tool and then return it to
the business unit’s supporting Human Resource representative. After one week, the
Human Resource representative contacted all engineers within the Applications job
function who had not completed the time allocation measurement survey and granted an
additional week extension to fill out and return a completed survey. After the second
deadline had passed, no additional surveys were collected.
Measurement/Measures/Design
A group of five subject matter experts (SMEs) participated in the development of
a work task inventory for the Applications engineering job function. Each SME was
responsible for creating a unique work task inventory which, upon completion, was
aggregated with the other task inventories to create a single cumulative inventory. Once
the cumulative inventory was complete, the SMEs deliberated over the list and eventually
added, removed, combined, and revised the task inventory until it was finalized. The
initial cumulative work task inventory was paired down to the 48-item inventory included
in the final version of the time allocation measurement tool. The SMEs then grouped the
final 48-item work task inventory into eight independent work task categories: Market
Strategy, Demonstration and Evaluation Boards, Reference Designs, Product
Development, Product Support and Sales Collateral, Customer Interface, Competitive
25
Analysis, and an Other category. For each individual work task, the SMEs created a brief
description of the activity associated with each work task and was included in the time
allocation measurement tool. A full list of the work tasks included in the time allocation
measurement tool, along with the abrief description of each work task, is provided in
Table 1. The goal of including work task descriptions in the measurement tool was to
standardize the definition of each work task. For each work task category and subsequent
list of individual work tasks, an “other” option was provided to capture any task or
activity not otherwise represented in the time allocation measurement tool.
The first question in the survey asked participants to estimate the total number of
hours worked in a typical week. Participants were then given a number of time options
ranging from “Between 25 and 35 hours” to “More than 65 hours” in 10-hour increments.
All Applications engineers are expected to work at least 25 hours per week so no option
was provided for working less than that amount of time. The purpose of estimating the
amount of time worked in a typical week was to account for the fact that some
participants only work 25 hours per week, whereas other participants work 65 hours per
week. If participants did not estimate the total of hours worked in a typical week, then
they were not permitted to continue to the next section of the survey.
For the second question, participants were instructed to indicate the percentage of
time spent in a typical month on each different work task category, using increments of
5% (5%, 10%, 15%, etc.). Asking the participants to indicate the percentage of time
spent in a typical month in increments of 5% was designed to aid participants in filling
out the measurement tool.
26
Table 1
Inventory of Work Tasks and Task Descriptions Grouped by Work Task Category
Market StrategyDeveloping Industry Expertise Activities geared toward understanding customer needs outside of the laboratory
Developing System Expertise Activities geared toward understanding customer needs by lab experimentation
New Product Idea Generation Generating, validating, and submitting an idea to the new product idea database
Markey Segment Strategy Development Activities associated with the research, development, preparation, and
implementation of Market Segment strategy
Product Strategy Development Activities associated with the research, development, preparation, and
implementation of a strategic business plan and strategy development
Demonstration & Evaluation BoardsSchematic Capture The full schematic development process through final review
PCB Layout and Review The full PCB layout process and review
Software Software development associated with demonstration and evaluation boards
PCB Evaluation and Testing The full PCB evaluation and testing process
User Documentation Creating comprehensive documentation for the purpose of documenting system
performance and/or developing other support collateral
PCB Manufacturing Documents Specification control documents, PCB test procedures, etc.
Production & Inventory Management Management of demonstration and evaluation kit inventory levels as well as
inventory management of necessary materials to build boards and kits
Reference DesignsPlatform Definition The process of defining the appropriate platform for the reference design
Schematic Capture The full schematic development process through final review
PCB Design The full PCB design process
Software Software development associated with reference design functionality
User Documentation Creating documentation around system performance
Characterization/Laboratory Evaluation Comprehensive system-level evaluation of all reference designs
PCB Manufacturing Documents Preparation of documentation packages including specification control documents,
PCB test procedures, etc.
Product DevelopmentProduct Requirements Specifications Phases 1 through 3 of the New Product Phase Review System
Application Notes/White Papers Creation of application notes and white papers related to a specific product
Datasheets Phases 4 and beyond of the New Product Phase Review System
Software Simulators Creation of software simulators used to model the product and/or technology
Product Evaluation Tools All evaluation tools other than an evaluation board used to test a product
Product Development Meetings All meetings related to the product development process
Applications Silicon Evaluation Formation of a product plan, evaluation of silicon in the lab, and subsequent
performance reports
Product Support & Sales CollateralDesigner's Guide and/or Selection Guide Creation of designer’s guides and/or selection guide used to model the product
Product Demonstration Kits All evaluation tools, other than evaluation boards, used to test a product
Training - Material Creation of training materials including analog seminars, FAE training, etc.
Training - Delivery Delivery of training materials including analog seminars, FAE training, etc.
Models - Spice/IBIS/Etc All evaluation models used to test a product and/or technology
User Information Sheets Comprehensive documentation for the purpose of reporting system performance
and/or developing support collateral
Customer InterfaceDirect Sales Support Demand creation involving indirect customer communication
Reactive - Design-In Suuport Direct customer support during the design-in phase, after product selection
Reactive - Problem Resolution Support of a PQA, etc
Proactive - Program Discovery Direct or indirect customer interaction to understand design cycles, etc.
Proactive - Demand Creation Demand creation support involving direct customer interaction prior to component
selection
Competitive AnalysisDatasheet comparison The process of comparing datasheets of competitive products
Laboratory Comparison The process of comparing product performance specifications in the laboratory
Report Generation Creating comprehensive reports for the purpose of documenting performance
Comprehensive Silicon Evaluation The process of comparing silicon specifications in the laboratory setting,
specifically includes the decapping process
27
In theory there is an exact percent for each work task category, but by keeping the
percentages grouped into 5% increments allowed for easier completion of the
measurement tool and easier comparison of the data. Participants were required to
allocate 100% of their time between the eight separate work task categories to ensure that
all of the data could be easily compared across the separate work task categories.
Furthermore, at work participants always have 100% of their time to allocate to
something, whether it is one specific work task or another. If a participant did not
allocate exactly 100% of their time across the separate work task categories, then they
were not permitted to continue to the next section of the survey. Due to the nature of the
Applications engineering job function, some work tasks are more common during certain
stages of the product development process, which is why study participants were
requested to provide time allocation data for a typical month.
Once participants had indicated the total percentage of time allocation for a
specific work task category, they were then given the option to indicate whether the work
task category was critical to the overall success of the organization, which was
specifically defined by the SMEs who built the work task inventory as the “achievement
of group-defined deliverables.” The purpose of assessing the criticality of each specific
work task from the perspective of the Applications engineer is to determine whether
significant gaps exist between the productivity of participants at different levels of work
experience, both inside and outside of the organization. Furthermore, collecting specific
work task criticality data provides an opportunity to correlate the amount of time spent on
work tasks with the tasks most frequently identified as being critical to the success of the
28
organization. For the purposes of this study, group-defined deliverables for the
Applications engineering job function were created by the SMEs and consisted of four
separate objectives: Strategic Development, Product Cycle Times, Product Design Wins,
and Customer Design Support. Each group-defined deliverable was included on the time
allocation measurement tool and also included a brief description to ensure that all survey
participants were using a standard definition of the deliverable metrics. On the actual
time allocation measurement tool, a cell was provided next to each work task category, as
well as each individual work task, and the participant was instructed to mark an “X” next
to each work task or category that they deemed to be critical to the achievement of group-
defined deliverables.
For the third question of the survey, each of the eight work task categories was
then split up into separate matrices that included a list of all individual work tasks under
each work task category. Then, based on a calculation incorporating the total number of
hours worked in a typical month and the percentage of time allocated to each work task
category collected earlier in the survey, participants were given a range of hours per
month to allocate to each individual work task. For example, if a participant indicated
that they worked “Between 45 and 55 hours” in a typical week and then indicated that
they spent 10% of their time in a given month of Market Strategy activities, then the
participant was provided with a time allocation range of 18 to 22 hours per month to
allocate to the specific work tasks under Market Strategy. A participant would then
assign these hours to the provided list of work tasks under Market Strategy until the total
time allocation hours fell within the predetermined time allocation range. If the assigned
29
total of time allocation hours did not fall within the predetermined range, then the
participant was not permitted to continue to the next section of the survey. The purpose
of providing a time allocation range for the participant was to ensure that the specific
number of hours assigned to each work task was relative to the overall percentage of time
allocated to that specific work task category.
Similar to question two of the survey, participants were also given the option to
indicate whether each specific work task was critical to the success of the overall work
task category. By also assessing the criticality of specific work tasks within the context
of each work task category, the key work tasks could be identified. The process
described above was repeated for all of the eight separate work task categories, until time
allocation data and criticality data were collected for each work task category. An
example of the time allocation measurement tool utilized in this study is provided in the
Appendix.
30
Results
As previously described, the collection of demographic information was kept to a
minimum to preserve anonymity and confidentiality. Subsequently, participants were
only asked to provide data on the total years of work experience, which is reported in
Table 2.
While some of the information reported here was previously been discussed in the
Method section, it is reported here again in greater detail as additional information related
to the years of previous and current work experience is relevant to the results discussion.
On average, the mean years of experience for participants prior to joining the current
Table 2
Descriptive Statistics of Years of Work Experience and Number of Hours Worked Per Week
Group N SD
Years of Work Experience (Previous) 61 100.0% 6.55 3.00 8.425 0.00 35.00
0-10 Years 45 73.8% 2.46 0.58 3.256 0.00 10.00
11-20 Years 11 18.0% 13.50 12.00 2.540 11.00 19.00
21+ Years 5 8.2% 28.03 27.00 5.867 21.17 35.00
Years of Work Experience (Current) 61 100.0% 7.22 6.42 6.308 0.00 33.50
0-10 Years 48 78.7% 4.71 6.42 2.880 0.00 10.25
11-20 Years 11 18.0% 13.80 12.58 2.611 11.50 18.33
21+ Years 2 3.3% 31.08 31.08 3.418 18.33 33.50
Years of Work Experience (Total) 61 100.0% 13.77 12.00 9.853 0.42 37.33
0-10 Years 27 44.2% 5.29 6.33 3.103 0.42 9.83
11-20 Years 20 32.8% 14.84 14.67 2.908 11.50 20.40
21+ Years 14 23.0% 28.57 27.58 5.421 21.58 37.33
Hours Worked Per Week
25-35 Hours/Week 2 3.3% -- -- -- -- --
35-45 Hours/Week 21 34.4% -- -- -- -- --
45-55 Hours/Week 32 52.5% -- -- -- -- --
55-65 Hours/Week 6 9.8% -- -- -- -- --
65+ Hours/Week 0 0.0% -- -- -- -- --
MaximumPercent (%) Mean (Yrs.) Median (Yrs.) Minimum
31
company was 6.55 years (SD = 8.43), ranging from zero previous experience to 35 years
of experience. A total of 45 participants, or 74%, reported having 10 years or less of
previous work experience, with a mean of 2.46 years of experience (SD = 3.26). Another
11 participants (18%) reported having between 11 and 20 years of previous work
experience, with a mean of 13.50 years of experience (SD = 2.54). The remaining 5
participants (8%) reported having 21 or more years of previous work experience, with a
mean of 28.03 years of experience (SD = 5.87). As evidenced by the significant number
of participants with less than 10 years of previous work experience and the relatively low
mean of 6.55 years of experience, it appears that the Applications engineers in the present
sample were likely to be hired right out of school or were early in their professional
careers.
Regarding the years of experience at the current company, the mean years of
experience was 7.22 years (SD = 6.31), ranging from zero experience to 33.50 years.
Again, the vast majority of participants (78%) reported having 10 years or less of work
experience at the current company, with a mean of 4.71 years of experience (SD = 2.88).
Of the remaining 13 participants, 11 reported having 11-20 years of work experience at
the current company, with a mean of 13.80 years of experience (SD = 2.61). Similar to
the previous work experience results, the majority of participants had less than 10 years
of work experience at the current company.
Upon examination of the combined results of work experience, both past and
present, the mean years of work experience was 13.77 years (SD = 9.85), with a
minimum of 0.42 years and maximum of 37.33 years of total work experience. At the
32
combined level, greater balance in terms of participant numbers was present between the
groups of participants who had 10 or less years of work experience (27), 11-20 years of
work experience (20), and 21+ years of work experience (14). While the mean years of
combined work experience for participants in the 10 years or less group was relatively
low at 5.29 (SD = 3.10), the fact that the means for the 11-20 group and 21+ years group
were 14.84 (SD = 2.91) and 28.57 (SD = 5.42) respectively helped bring the overall mean
up to 13.77 years of combined experience (SD = 9.85).
With regard to the number of hours worked in a typical week, the median
response was “between 45 and 55 hours per week” and accounted for 52% of all
responses. An additional 34% of participants indicated that they worked “between 35 and
45 hours per week.” Overall, 86% of the total participants indicated that they worked
between 35 and 55 hours per week. The fact that 86% of participants indicated that they
work within this combined range of 35 to 55 hours per week provides support for the
overall idea that the majority of participants worked roughly the same number of hours
per week. The full distribution of hours worked per week is provided in Table 2.
Time Allocation
Table 3 displays the mean time allocation distribution across the eight different
work task categories for all participants, as well as time allocation distributions sorted by
total years of work experience and number of hours worked per week. As can be seen,
the overall time allocation distribution suggests that certain work task categories received
more time allocation than others. The two work task categories receiving the lowest
amount of time allocation at 4% each were Market Strategy and Competitive Analysis.
33
The work task category receiving the highest amount of time allocation at 30% was
Product Development. Aside from the Other work task category, which received a time
allocation rating of 7%, the remaining four work task categories (Demonstration and
Evaluation Boards, Reference Designs, Product Support and Sales Collateral, and
Customer Interface) all received time allocation ratings between 11% and 20%.
Overall these results indicate that each work task category received a decent portion of
time allocation, which could be interpreted to support the validity of the overall work task
inventory.
Table 3
Time Allocation Distribution Across Work Task Categories and Frequency of Critical Work Tasks
Group
Market Strategy
Dem
onstration &
Evaluation Boards
Reference D
esigns
Product Developm
ent
Product Support &
Sales Collateral
Custom
er Interface
Com
petitive Analysis
Other
All Participants (N=61) 4% 20% 12% 30% 11% 12% 4% 7%
Years of Work Experience
0-10 Years (N=27) 2% 17% 13% 33% 12% 10% 5% 8%
11-20 Years (N=20) 6% 23% 11% 30% 10% 11% 4% 5%
21+ Years (N=14) 5% 20% 9% 25% 11% 19% 4% 7%
Hours Worked Per Week
25-35 Hours/Week (N=2) 0% 20% 50% 0% 0% 0% 20% 10%
35-45 Hours/Week (N=21) 2% 16% 9% 40% 13% 8% 4% 9%
45-55 Hours/Week (N=32) 5% 23% 10% 28% 10% 15% 4% 5%
55-65 Hours/Week (N=6) 7% 17% 15% 17% 15% 18% 5% 7%
65+ Hours/Week (N=0) 0% 0% 0% 0% 0% 0% 0% 0%
Critical Work Task Selection
Strategic Development (N=83) 26 4 9 4 3 20 17 0
Product Cycle Times (N=70) 2 20 3 30 8 4 2 1
Product Design Wins (N=136) 14 17 22 9 25 28 18 3
Customer Design Support (N=109) 4 21 19 7 24 32 2 0
Work Task Total (N=397) 46 62 53 50 60 83 39 4
34
Looking at the time allocation distribution of participants organized by total years
of work experience, the results suggest that the least experienced participants spent the
greatest percentage of their time (33%) on activities related to Product Development,
with the next highest time allocation rating being 17% for Demonstration and Evaluation
Boards. While the most experienced participants also spent the greatest percentage of
their time on Product Development, the overall percentage of time (25%) was less than
the least experienced participants spent (33%). Additionally for the most experienced
participants, the difference between the highest and second highest rated work task
category (5%) was significantly less than 16% for the least experienced participants.
Findings of this kind indicate that less experienced Applications engineers tend to have
more focused job responsibilities and less exposure to other work task categories that
benefit greatly for previous work experience, such as Customer Interface, which the most
experienced Applications engineers reported the greatest amount of time allocated at
19%, versus 11% for the 11-20 years of experience group and 10% for the 0-10 years of
experience group. While the least experienced participants still reported time allocation
needs in each of the eight work task categories, the finding stated above prompts an
interesting question regarding the potential differences in job functions for more or less
experienced engineers. Similar to the progressive increase in Customer Interface time
allocation for more experienced participants, the reported time allocation associated with
Reference Designs progressively decreased as a function of work experience, although
not significantly at only 2% each time. Such results provide a good indication of how
work task category time allocation evolves over time for Applications engineers as they
35
gain relevant work experience, inside and outside of the organization. Although it is
worth mentioning that not all of the work task categories showed such uniform
movement depending on the number of hours worked per week, further measurement
should be done in similar engineering disciplines to see if similar results are found.
Regarding the time allocation distribution of participants organized by total hours
worked per week, the results show that the vast majority of Applications engineers (86%)
worked between 35 and 55 hours per week and allocated the greatest percentage of time
to Product Development (40% for 35-45 hours per week participants and 28% for 45-55
hours per week participants). Only at the 55-65 hours per week level was the allocation
of time for Product Development (17%) not the greatest percentage of all work task
categories. Instead, the highest percentage of time allocation (18%) was allocated to
Customer Interface work task activities. It is also worth noting that the distribution of
time at the 55-65 hours worked per week level was much more balanced than the other
hours worked per week levels, with five of the eight work task categories having time
allocation percentages between 15% and 18%. The overall distribution balance at the 55-
65 hours worked per week level varied considerably from the 35-45 hours per week level
where the next highest percentage after the 40% allocation to Product Development was
16% for Demonstration and Evaluation Boards. Furthermore, the only other work
category with a time allocation percentage over 10% for the 35-45 hours per week group
was Product Support and Sales Collateral at 13%. The remaining work task categories
ranged in time allocation percentages from 9% for Reference Designs to 2% for Market
Strategy.
36
Similar to the trend referenced above regarding the increased time allocation of
Customer Interface activities for more experienced participants, numerous trends
associated with the number of hours worked per week were identified. In the case of five
of the eight work task categories, the average time allocation either increased or
decreased in direct relation to the number of hours worked per week, with the exceptions
being Demonstration and Evaluation Boards, Product Support and Sales Collateral, and
Other. Market Strategy increased from 2% to 7%, Reference Designs increased from 9%
to 15%, Customer Interface increased from 8% to 18%, and Competitive Analysis
increased from 4% to 5%. On the other hand, the average time allocation percentage for
Product Development decreased from 40% to 17% as the number of hours worked per
week seemed to increase.
The distribution trends outlined above also support the idea that different work
activities require varying amounts of time to complete. For example, the fact that the
time allocation of the Customer Interface work task category increased from 8% to 18%
as the total number of hours worked per week increased from 35-45 hours per week to
55-65 hours per work indicates that participants are more likely to allocate time to
Customer Interface work task activities after devoting time to more product-based work
tasks. The more time a participant had to allocate, meaning as the indicated number of
hours worked per week increased, the greater the distribution balance between activities.
When participants had fewer hours to allocate, the likelihood of the time allocation
distribution being skewed toward certain work task categories increased. As previously
37
indicated, this conclusion is drawn from the 40% allocation for Product Development for
the group members who indicated that they worked between 35-45 hours per week.
Also reported in Table 3 are the frequency data related to which work task
categories were reported to be most critical to the achievement of group-defined
deliverables. The work task category receiving the greatest support as being critical to
the overall success of the Applications group was Customer Interface, receiving 83
positive selections, which means that participants positively indicated on the time
allocation measurement tool that a work task contributed significantly to the achievement
of group-defined deliverables. The next highest rated work task category at 62 positive
selections was Demonstration and Evaluation Boards, followed by Product Support and
Sales Collateral (60), Reference Designs (53), Product Development (50), Market
Strategy (46), Competitive Analysis (39), and Other (4) respectively. Upon examination
of the positive selections for the Customer Interface work task category, what
immediately stands out as a possible explanation for why it received the greatest number
of positive selections is that it is the only category to receive at least 20 positive
selections in three out of the four possible outcome deliverables included on the time
allocation measurement tool (Strategic Development, Product Design Wins, and
Customer Design Support). The combination of positive support in multiple outcome
deliverables and the highest total of positive selections for any single outcome deliverable
(Customer Design Support - 32) help explain why Customer Interface finished 21
positive selections ahead of the next highest total (62) belonging to Demonstration and
Evaluation Boards. Interestingly, of all the work task categories (not including Other) the
38
two that received the lowest percentage of overall time allocation (Market Strategy and
Competitive Analysis) at 4% each also received the fewest critical task positive selections
at 46 and 39, respectively.
Table 4 represents the separate time allocation distributions within each of the
eight separate work task categories, as well as the critical task positive selections within
each work task category. Quantitative time data related to the overall time allocation
within each work task category is reported as a sum total, as well as a corresponding
percentage of the total hours allocated to each specific work task. Similarly, qualitative
criticality data is reported for each work task as both a sum total of positive selections
and as a percentage of the total critical task positive selections within each work task
category.
Upon review of the data presented in Table 4, the time allocation distribution
within each work task category proved to often be very unique, with some work task
categories having one specific work task account for a significant portion of the time
distribution, and others having the time distribution evenly spread among several
individual work tasks. For example, Product Support and Sales Collateral is a good
example of a work task category where the time distribution was mainly concentrated on
one work task, specifically the “Training – Material” work task which account for 470
total hours per month and 37% of the total time allocation for the entire work task
category. The next highest single work task was “Designer’s Guides and/or Selection
Guide,” which received a total of 194 hours, or 15% of the total time allocation, for
Product Support and Sales Collateral, resulting in a drop off of 22%. Similarly, the 21
39
critical task positive selections for “Training – Material” (36%) far exceeded the next
highest critical task positive selection total of 12 (21%), which corresponded to the
“Product Demonstration Kits” work task. These results are interesting in that they show
that not only were participants spending the majority of their Product Support and Sales
Collateral hours working on “Training – Material,” but also that they believe this to be
the most critical task within the work task category. Without data assessing the perceived
importance of each work task, it would not be possible to determine whether the
participants believe the current distribution of time is contributing positively to the
success of the organization.
An example of a work task category where the distribution of time allocation was
evenly spread among several individual work tasks is the Reference Designs work task
category, which had time allocation totals ranging from 126 to 327 hours, or 10% to 26%,
for five of the seven individual work tasks within the category. The relative balance
across the work tasks within this work category suggests that each individual work task
requires a similar amount of time to complete the task. The highest time allocation of
327 hours per month was associated with the “PCB Manufacturing Documents” work
task, whereas the lowest time allocation of 53 hours was associated with the
“Characterization/Laboratory Evaluation” work task. Interestingly, when looking at the
critical task positive selection data for the Reference Designs work task category, a
different result from the Product Support and Sales Collateral is present.
40
Table 4
Time Allocation Distribution Within Work Task Categories and Frequency of Critical Work Tasks
Market Strategy (4%) 503 83Developing Industry Expertise 178 35% 24 29%
Developing System Expertise 146 29% 30 36%
New Product Idea Generation 78 16% 13 16%
Markey Segment Strategy Development 44 9% 7 8%
Product Strategy Development 42 8% 9 11%
Demonstration & Evaluation Boards (20%) 2321 93PCB Evaluation and Testing 573 25% 22 24%
PCB Layout and Review 560 24% 23 25%
Schematic Capture 364 16% 21 23%
User Documentation 302 13% 12 13%
Software 231 10% 8 9%
PCB Manufacturing Documents 145 6% 4 4%
Production & Inventory Management 103 4% 3 3%
Reference Designs (12%) 1274 82PCB Manufacturing Documents 327 26% 4 5%
Schematic Capture 235 18% 14 17%
Platform Definition 182 14% 15 18%
PCB Design 182 14% 11 13%
User Documentation 126 10% 14 17%
Software 88 7% 6 7%
Characterization/Laboratory Evaluation 53 4% 18 22%
Product Development (30%) 3476 103Software Simulators 1131 33% 6 6%
Application Notes/White Papers 877 25% 17 17%
Applications Silicon Evaluation 442 13% 25 24%
Datasheets 321 9% 29 28%
Product Evaluation Tools 267 8% 3 3%
Product Requirements Specifications 242 7% 17 17%
Product Development Meetings 114 3% 6 6%
Product Support & Sales Collateral (11%) 1275 58Training - Material 470 37% 21 36%
Designer's Guide and/or Selection Guide 194 15% 11 19%
Product Demonstration Kits 168 13% 12 21%
Training - Delivery 151 12% 8 14%
User Information Sheets 135 11% 2 3%
Models - Spice/IBIS/Etc 89 7% 4 7%
Customer Interface (12%) 1491 70Reactive - Problem Resolution 586 39% 16 23%
Reactive - Design-In Suuport 331 22% 17 24%
Direct Sales Support 296 20% 15 21%
Proactive - Demand Creation 139 9% 11 16%
Proactive - Program Discovery 103 7% 11 16%
Competitive Analysis (4%) 493 45Datasheet comparison 197 40% 12 27%
Laboratory Comparison 152 31% 18 40%
Report Generation 81 16% 9 20%
Comprehensive Silicon Evaluation 48 10% 6 13%
Other (7%) 749 8Meetings, Email, General Communication 555 74% 4 50%
Training, Workshops, Mentoring Activities 128 17% 4 50%
% of Critical
Task Selection
Work Task Category
(% of Overall Time Distribution)
Time Allocation
(Hrs.)
% of Time
Allocation
Critical Task
Selection
41
The work task receiving the greatest allocation of time, “PCB Manufacturing
Documents,” at 26% actually received the lowest total of critical task positive selections
at 4 (5%). Conversely, the work task receiving the lowest total time allocation,
“Characterization/Laboratory Evaluation,” at 53 total hours actually received the highest
number of critical task positive selections at 18, or 22% of the total. These results
provide support for the argument that perhaps the current time allocation distribution
within the Reference Designs work task category is not allocated properly to best
maximize success with the organization. By collecting data on the criticality of a work
task, it is now possible to determine whether the participants feel their time is best being
allocated within each separate work task category.
42
Discussion
Despite the challenges of conceptualizing productivity, specifically in the
workplace, it remains to be one of the central goals of all organizations. Most often
productivity is conceptualized and defined using a combination of efficiency and
effectiveness measures (Forrester, 1993; Gomar, et al., 2002; Gowda & Chand, 1993;
Koss, et al., 1993; Miller, 1984; Misterek, et al., 1992; Pritchard, 1995; Thadhani, 1984).
As previously explained, efficiency at its most basic level is a ratio of outputs to inputs,
with the goal being to always increase outputs while decreasing inputs, thus increasing
the efficiency of the group, team, or organization (Pritchard, 1995; Quinn, 1978). On the
other hand, effectiveness is a ratio of outputs to goals, where the goal is not necessarily to
increase efficiency, but instead focus on achieving the goals of the group, team, or
organization (Pritchard, 1995; Quinn, 1978). If the task of a group is to build ten widgets
in ten minutes, then the efficiency conceptualization of productivity would dictate
building more widgets in less time, say twenty widgets in five minutes. However, what if
the goal is to not only build ten widgets in ten minutes, but also to build widgets that last
ten years? Evaluating productivity from an effectiveness perspective would dictate that if
building twenty widgets in five minutes caused the widgets to only last five years instead
of ten, then the original goal was not achieved and the overall assessment of productivity
would be negatively affected. It is worth noting that in this example, and also in the
majority of productivity research in the workplace, the data used to determine efficiency
and effectiveness were quantitative in nature. Quantitative data collection lies at the heart
43
of almost all productivity research because it provides an objective dataset by which to
assess productivity.
From this conceptualization of productivity including both efficiency and
effectiveness, and subsequent emphasis on quantitative data collection, comes the
concept of utilizing time allocation as an assessment of productivity. As previously
described, time allocation measurement in the workplace seeks to capture the distribution
of time allocated to a set of work task categories and separate work tasks in a particular
job function. In the present study, we proposed a unique time allocation measurement
tool of productivity designed to assess the time allocation distribution, across and within
a range of work task categories, associated within the job function of Applications
engineering.
While the existing time allocation literature includes several studies utilizing
different time allocation assessment techniques (Borman, et al., 1992; Dierdorff, et al.,
2003; Gross, 1984; Lindell, et al., 1998; Miller, 1986), there still exist a number of
limitations in the current literature that were addressed in the present study. The first
limitation being that often time allocation data are collected via observational research
methods. There are several limitations with the observational method, one of them being
that data can only be collected while the subject or participant is being observed and,
depending on the population being studied, this can be a significant reliability issue since
there are observable behaviors that could be missed when the researcher is not observing
the population (Gross, 1984). Similarly, if a subject or participant does not display a
44
specific behavior, the researcher has no way of tracking the missed behavior because only
observed behaviors are recorded.
In response to this limitation, a work task inventory was created for the present
study utilizing subject matter experts within the organization and job function to ensure
that the task inventory was comprehensive and representative of the Applications
engineering job function. By doing so, it became possible to determine which tasks or
behaviors were being displayed and which ones were not. The results of the present
study support the use of a work task inventory, as all 48 of the work tasks included in the
final inventory were allocated at least some amount of time. If an irrelevant work task
had been included in the task inventory, then theoretically it would not have received any
time allocation.
Another limitation present in the existing time allocation research addressed in the
present study had to do with the data collection of the number of hours worked per week
by participants. In no previous study of time allocation in the workplace had the number
of hours worked per week been factored into the overall distribution of time. Taking into
account the number of hours worked for each participant allowed for the direct
comparison of the time distribution among participants who worked between 25 and 35
hours per week and participants who worked 55 to 65 hours per week. Furthermore,
upon examination of the overall time allocation results sorted by the number of hours
worked per week, there were significant differences in the allocation of time among the
work task categories of the multiple groups. In terms of productivity, these findings
suggest that as the number of hours worked per week increases, the overall distribution of
45
time allocation should become more balanced, thus increasing the percentage of time
allocated for work tasks that would otherwise not be engaged in with as much frequency
if the number of hours worked per week were reduced.
Furthermore, the results revealed that Market Strategy, Reference Designs,
Customer Interface, and Competitive Analysis all had time allocation percentages that
increased as the total number of hours worked per week increased. This finding is both
interesting and counterintuitive as you might expect that an increase in the total number
of hours worked would not have any significant impact on the overall distribution of time
among a group of work task categories. Simply working more hours over the course of a
week would intuitively lead to spending more total hours working on the same tasks, but
not necessarily a significant change to the percentage of time allocated to each one
individually. However, this was not the case as the increase in the total number of hours
worked per week actually affected the overall distribution of time among the different
work task categories. What is clear from looking at the results is that an increase in hours
worked per week allowed for greater time allocation balance among the various work
task categories. This is further evidenced by the percentage disparity present in the single
highest allocated work task category for each of the four hour groups: 50% – Reference
Designs for the 25 to 35 hours per week group, 40% – Product Development for the 35 to
45 hours per week group, 28% – Product Development for the 45 to 55 hours per week
group, and 18% – Customer Interface for the 55 to 65 hours per week group. The
trending downward of the single highest allocated work task category for each group
signals greater balance of time distribution among the remaining work task categories at
46
the higher hourly work totals, as well as supports the notion that certain work tasks are
only allocated time once other work task responsibilities have been satisfied. In practical
application, this finding suggests a possible reallocation of either resources or incentives
to these affected work task categories to increase the respective time allocation
percentages if it is determined that these lower-allocated work task categories are of
significant value to the success of the organization. In an effort to better understand why
certain work tasks received more or less time allocation as the number of hours worked
per week increased, future research should examine the possibility that the number of
hours worked per week is positively correlated with organizational rank or job level
within the organization. In the present study an similar attempt was made by collecting
data on the total years of previous and current work experience, but ultimately it is not
the same data.
Related to this issue of perceived value to the success of the organization, an
additional contribution of the present study was the inclusion of a perceived criticality
measure. This measurement of perceived criticality applied to not only each work task
category, but also each specific work task within the work task categories. The purpose
of including such a measure in the time allocation tool was to provide a second level of
information for each time allocation rating and allow the participant to indicate which
work tasks were most critical by proactively marking each “critical task” on the
measurement tool with an “X.” In the present study, the goal of utilizing perceived
criticality data to determine why certain tasks were allocated more or less time was
achieved. As previously described, knowing whether a participant believed a work task
47
to be critical to the success of the organization made the subsequent analysis of the
quantitative time data much easier and more robust.
Yet, despite the contributions of this study in the areas of productivity
measurement and time allocation assessment, much work remains to be done. While this
time allocation assessment tool goes beyond the established literature on the time
allocation measurement, a potential expansion of the tool would be to include an “Ideal”
time allocation distribution along side the “Actual” time distribution. This would allow
for the direct comparison of the perceived differences between what is currently being
allocated and what ought to be allocated. An “Ideal” time distribution in this scenario
would tie directly in with the concept of an “Ideal” productivity level for a given
population. Ultimately the goal is to relate time allocation data back to productivity and
by collecting data on the perceived “Ideal” time distribution, this becomes much easier
because productivity is often conceptualized in terms of “Ideal” productivity.
Additionally, work task criticality measures would also help confirm or deny the
proposed “Ideal” time allocation distribution.
Ultimately, the purpose of the present study was to create a unique and reliable
time allocation measurement tool to assess the overall productivity of a group of
Applications engineers at a large tech company in the semiconductor industry. By
looking at the time allocation distribution across a set of work task categories and by
assessing the criticality of each work task, gaps start to become apparent between what is
currently being allocated and which tasks are most frequently being positively selected as
48
critical. These gaps represent the productivity improvement areas that exist within the
organization.
The present study also succeeded in utilizing subject matter experts in the initial
creation of the work task inventory, as well as collecting quantitative time data that
would allow for the creation of an overall time allocation distribution across all work task
categories and also within each separate work task category. The present study
succeeded in factoring in the total number of hours worked in a typical week, which
ultimately revealed interesting results related to the evolution of time allocation as the
total number of hours worked per week increased. And finally, the present study also
succeeded in assessing qualitative criticality data associated with each of the work task
categories and individual work tasks, which helped provide the contextual evidence
necessary to begin to decipher the complex relationship between time allocation and
productivity in the present study. The process of assessing productivity through the use
of time allocation measurement is not an easy task, but through the use of a successful
time allocation measurement tool in the present study, it is possible to begin to connect
the dots between time allocation and productivity and ultimately tap into the full potential
of the organization and its workforce.
49
References
Bailey, D.E. (2000). Modeling work group effectiveness in high-technology
manufacturing environments. IIE Transactions, 32, 361-368.
Borman, W.C., Dorsey, D., & Ackerman, L. (1992). Time-spent responses as time
allocation strategies: Relations with sales performance in a stockbroker sample.
Personnel Psychology, 45, 763-777.
Chand, S., Moskowitz, H., Novak, A., Rekhi, I., & Sorger, G. (1996). Capacity allocation
for dynamic process improvement with quality and demand considerations.
Operations Research, 44, 964-975.
Chu, S., & Lin, C. (1993). A manpower allocation model of job specialization. Journal of
the Operational Research Society, 44, 983-989.
Dierdorff, E.C., & Wilson, M.A. (2003). A meta-analysis of job analysis reliability.
Journal of Applied Psychology, 88, 635-646.
Forrester, J.W. (1993). Low productivity: it is a problem or merely a symptom?
Handbook for productivity measurement and improvement, Cambridge:
Productivity Press.
Gomar, J.E., Haas, C.T., & Morton, D.P. (2002). Assignment and allocation optimization
of a partially multiskilled workforce. Journal of Construction Engineering and
Management, 128, 103-109.
Gowda, R.G., & Chand, D.R. (1993). An exploration of the impact of individual and
group factors of programmer productivity. ACM Conference on Computer
Science, 338-345.
50
Gross, D. (1984). Time allocation: A tool for the study of cultural behavior. Annual
Review of Anthropology, 13, 519-558.
Huy, Q.N. (2001). Time, temporal capability, and planned change. Academy of
Management Review, 26, 601-623.
Ilgen, D.R. & Klein, H. (1988). Individual motivation and performance: Cognitive
influences on effort and choice. In John P. Campbell, Richard J. Campbell, and
Associates (Eds.), Productivity in organizations: New perspectives from industrial
and organizational psychology (pp. 143-176). San Francisco: Jossey-Bass.
Kanfer, R. & Ackerman, P. (1989). Motivation and cognitive abilities: An
integrative/aptitude-treatment interaction approach to skill acquisition. Journal of
Applied Psychology, 74, 657-690.
Koss, E. & Lewis, D.A. (1993). Productivity or efficiency – measuring what we really
want. National Productivity Review, 12, 273-295.
Lord, R.L. (2002). Traditional motivation theories and older engineers. Engineering
Management Journal, 14, 3-7.
Lindell, M.K., Clause, C.S., Brandt, C.J., & Landis, R.S. (1998). Relationship between
organizational context and job analysis task ratings. Journal of Applied
Psychology, 83, 769-776.
Miller, D.M. (1984). Profitability = productivity + price recovery. Harvard Business
Review, May-June, 145-153.
Miller, M.D. (1986). Time allocation and patterns of item response. Journal of
Educational Measurement, 23, 147-156.
51
Minge-Klevana, W. (1980). Does labor time decrease with industrialization? A survey of
time-allocation studies. Current Anthropology, 21, 279-298.
Misterek, S., Dooley, K., Anderson, J. (1992). Productivity as a performance measure.
International Journal of Operations and Production Management, 12, 29-45.
Norman, R.J., & Nunamaker, Jr., J.F. (1989). CASE productivity perceptions of software
engineering professionals. Communications of the ACM, 32, 1102-1108.
Pinsonneault, A., & Rivard, S. (1998). Information technology and the nature of
managerial work: From the productivity paradox to the Icarus paradox. MIS
Quarterly, 22, 287- 311.
Pritchard, R.D. (1992). Organizational productivity. In Dunnette, M.D. & Hough, L.M.
(Eds.) Handbook of industrial and organizational psychology, Vol. 3, (2nd ed.).
Palo Alto, CA: Consulting Psychologists Press, pp. 443-471.
Pritchard, R.D. (Ed.). (1995). Productivity measurement and improvement:
Organizational case studies. New York: Praeger Publishers/Greenwood
Publishing Group, Inc.
Pritchard, R.D., Harrell, M.M., DiazGranados, D., & Guzman, M.J. (2008). The
productivity measurement and enhancement system: A meta-analysis. Journal of
Applied Psychology, 93, 540-567.
Quinn, R.E. (1978). Productivity and the process of organizational improvement: Why
we cannot talk to each other. Public Administration Review, 38, 41-45.
Sanchez, J.I. (2000). Adapting work analysis to a fast-paced and electronic business
world. International Journal of Selection and Assessment, 8, 207-215.
52
Schmidt, A.M., & DeShon, R.P. (2007). What to do? The effects of discrepancies,
incentives, and time on dynamic goal prioritization. Journal of Applied
Psychology, 92, 928-941.
Sen, I. (1988). Class and gender in work time allocation. Economic and Political Weekly,
23, 1702-1706.
Sink, D.S., Tuttle, T.C. (1989). Planning and measurement in your organization of the
future. Norcross, U.S.A.: Industrial Engineering and Management Press, Ch. 5,
170-184.
Sink, D.S. and Smith, G.L., Jr. (1994). The influence of organizational linkages and
measurement practices on productivity and management. In Harris, D.H.,
Goodman, P.S. and Sink, D.S. (Eds.) Organizational linkages: Understanding the
productivity paradox (pp.131-160). National Academy Press, Washington
D.C.,131-160.
Singh, H., Motwani, J., Kumar, A. (2000). A review and analysis of the state of the art
research on productivity measurement. Industrial Management and Data
Systems, 100, 234-241.
Sousa-Poza, A., Schmid, H., & Widmer, R. (2001). The allocation and value of time
assigned to housework and child-care: An analysis for Switzerland. Journal of
Popular Economics, 14, 599-618.
Tangen, S. (2002). Understanding the concept of productivity. Proceedings of the 7th
Asia-Pacific Industrial Engineering and Management Systems Conference,
Taipei, December.
53
Thadhani, A.J. (1984). Factors affecting programmer productivity during application
development. IBM Systems Journal, 23, 19-35.
Tuttle, T.C. (1981). Productivity measurement methods: Classification, critique, and
implications for the Air Force. (AFHRL-TR-81-9). Brooks AFB, TX: Manpower
and Personnel Division, Air Force Human Resources Laboratory.
Twedt, D.W. (1966). The current marketing questions. Journal of Marketing, 30, 63-64.
Vosburgh, J., Curtis, B., Wolverton, R., Albert, B., Malec, H., Hoben, S., & Liu, Y.
(1984). Productivity factors and programming environments. IEEE, 143-152.
54
Appendix
Time Allocation Measurement Tool
Directions:
Range of Hours Worked
00000
Question #2. In a typical month...
Task Categories
Product
Design
Wins
Strategic
Development
Customer
Design
Support
Other (Specify):
Question #3. In a typical month…
(1 / 8)
Market Strategy
Critical
Tasks
Range of hours to allocate: 0 to 0
Other (Specify):
Total allocated hours: 0 0 0
Question #1. Estimate the total number of hours worked in a typical week: (Mark with an 'x')
Between 25 and 35 hours
Between 35 and 45 hours
Actual
Percent (%)
- Indicate percentage of time spent on activities grouped by category: (Total = 100%)
- Indicate which task catagories are critical to achieving group deliverables: (Mark with an 'x')
- When entering percents (%), only use multiples of 5 (5%, 10%, 15%, etc.)
Demo & Evaluation Boards
Market Strategy
Reference Designs
Product Development
Product Support/Sales Collateral
Customer Interface
Competitive Analysis
Time/Month
(hrs.)
Developing System Expertise
- Indicate number of hours spent on specific work tasks
- Indicate which 1 or 2 work tasks are most critical to achieving group deliverables: Mark with an x
- When entering hours, numbers do not need to be multiples of 5 (2 , 8, 10, etc. are acceptable)
Product Strategy Development
Developing Industry Expertise
New Product Idea Generation
KMS Strategy Development
Between 55 and 65 hours
More than 65 hours
Between 45 and 55 hours
- For each question…
- Only enter data into the yellow cells
- If data is entered correctly, the bottom cell will be green
- Be as honest and accurate as possible when estimating the number of hrs spent on work activities
No
0%
- For clarification on work categories or tasks, move cursor over cells to access additional notes
- For clarification on work categories or tasks, move cursor over cells to access additional notes
Critical to achieving deliverables
Product
Cycle
Times
Choose
One:
- If data is entered incorrectly, the bottom cell will be red
- Survey results are strictly confidential and will not be reported as individual results
- For clarification on work category or tasks, move cursor over cells to access additional notes