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

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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

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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