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Factors associated with an HPT professional's choice to solve work motivation problems

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Factors associated with an HPT professional’s choice to solve work motivation problems

_____________

A Dissertation Presented to the
FACULTY OF THE SCHOOL OF EDUCATION
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree of
DOCTOR OF EDUCATION
(Human Performance Technology)

_____________

April 2000

© Donna J. Price

iii

DEDICATION

This dissertation is dedicated to my husband and daughter, John and Madison
Rose Figueroa. During the last several years, my husband has provided me with love,
understanding, and encouragement with no expectation of anything in return; except that
I finish this work.

iv
ACKNOWLEDGMENTS

First, I acknowledge the work of my dissertation committee. I am extremely
grateful to my chairman, Dr. Dennis Hocevar, for his support, guidance, and
encouragement during this process. Thanks to Dr. Harry O’Neil for his suggestions on
improving the quality of this study. Special thanks to Dr. Daniel Blair, whose coaching
and persistence made the timeliness of this study possible.
Collecting the data would not have been possible without the sponsors. Again,
the contribution of Dr. Dennis Hocevar’s as the representative from the University of
Southern California (USC) was invaluable. Partnering with a prominent university was
advantageous to he other participating organizations, namely, International Society for
Performance Improvement (ISPI) and The Saratoga Group. Thanks to Dr. Roger
Addison, Director of Human Performance Technology of ISPI for offering a listing of
some of their members to invite to participate in this study. Having his name offered
acceptance of this study to the ISPI and American Society for Training and Development
(ASTD) member participants. The process of implementing the web-based questionnaire
survey was extremely smooth. Thanks to Steve Randesi, Vice President and Chief
Technology Officer of Saratoga Group for making this happen.
Without input from subject matter experts, this study would not have been as
successful. In spite of extremely busy schedules, special thanks to the following for
contributing their expertise: Richard E. Clark, Fred Estes, Danny Langdon, Jamie
Mulkey, Sharon Shrock, Brenda Sugrue, Kathleen Whiteside.

v
Lastly, I am extremely indebted to Daniel Blair, my partner and friend. Daniel is
a fellow USC cohort, a Hewlett-Packard coworker, and an associate in HPT projects. A
quote comes to mind by Helen Keller: "Be of good cheer. Do not think of today's failures,
but of the success that may come tomorrow. You have set yourselves a difficult task, but
you will succeed if you persevere; and you will find joy in overcoming obstacles.
Remember, no effort that we make to attain something beautiful is ever lost." (Cook,
1997, p. 495).

vi
TABLE OF CONTENTS
Dedication ……………………………………………………………………... iii
Acknowledgments ……………………………………………………………... iv
List of Figures …………………………………………………………………. ix
List of Tables …………………………………………………………………... x
Abstract ………………………………………………………………………... xi

I. PURPOSE OF THE STUDY

Introduction ……………………………………………………………………. 1
Importance of the Study ……………………………………………………….. 2
Operational Definition of Terms ………………………………………………. 5
Human Performance ………………………………………………………… 5
Performance Intervention …………………………………………………… 6
Work Motivation ……………………………………………………………. 7
Research Hypotheses ………………………………………………………... 8
Organization of the Remainder of the Dissertation ……………………………. 9

II. MOTIVATION: A BRIEF REVIEW OF THE LITERATURE

Motivation Theories and Models ……………………………………………… 12
Value-Expectancy Theory …………………………………………………. 13
Motivational Systems Theory (MST) ……………………………………… 14
Self-Efficacy Theory ………………………………………………………. 15
CANE Model ………………………………………………………………. 16
Motivation Constructs Selected for the Study ………………………………… 19
Self-Efficacy ……………………………………………………………….. 20
Task Value …………………………………………………………………. 20
Task Choice ………………………………………………………………... 21
Summary of the Motivation Research Literature ……………………………… 21

III. METHODS AND PROCEDURES OF THE STUDY

Design …………………………………………………………………………. 27
Participants …………………………………………………………………….. 28
Measures and Instruments ……………………………………………………... 31
Data Collection and Analysis Procedures ……………………………………... 35
Reliability and Item Analysis ………………………………………………….. 37

vii

IV. RESULTS OF THE STUDY

Descriptive Statistics …………………………………………………………... 39
Correlational Analysis ………………………………………………………….41

V. SUMMARY, CONCLUSIONS, AND IMPLICATIONS

Summary ………………………………………………………………………. 46
Conclusions ……………………………………………………………………. 49
Additional Findings ……………………………………………………………. 50
Implications ……………………………………………………………………. 53

REFERENCES …………………………………………………………………56

APPENDICES
Appendix A – Invitation to Participate in the Study E-Mail ………………. 60
Appendix B – Motivated Strategies for Learning Questionnaire (MSLQ)
Questionnaire Items ………………………………...
61
Appendix C – Questionnaire Survey ………………………………………. 62
Appendix D – Pie chart of survey participants

viii
LIST OF FIGURES
Figure 1. A Work Motivation Model ……………………………………………… 5
Figure 2. Research Hypothesis Matrix ……………………………………………. 8
Figure 3. Relationship Diagram of the Study Hypothesis ………………………… 9
Figure 4. Motivation System Theory ……………………………………………… 15
Figure 5. CANE Model of Factors Influencing Task Commitment ………………. 17
Figure 6. Sample Scenario ………………………………………………………… 33
Figure 7. Sample Items ……………………………………………………………. 34

ix
LIST OF TABLES
Table 1. Invitation to Participate in the Study, by Professional Organization …… 31
Table 2. Demographic Data Requested From Survey Respondents ……………... 32
Table 3. Survey Participants’ Work Experience …………………………………. 32
Table 4. Survey Participants’ Professional Society Membership ………………... 33
Table 5. Questionnaire Administration Process ………………………………….. 39
Table 6. Cronbach α for the Self-Efficacy and Task Value Scales ………………. 40
Table 7. Means and Standard Deviation of Self-Efficacy and Task Value Items
for each Scenario ………………………………………………………... 42
Table 8. Repeated Measures Multivariate Analysis of Variance (MANOVA) ….. 44
Table 9. Frequency of the Task Choice to Solve Human Performance Class
Problems …………………………………………………….…………... 41
Table 10. Pearson Product-Moment Correlation for the Self-Efficacy and Task
Value Scales within each of the three Human Performance Improvement
Scenarios ………………………………………………………………... 46
Table 11. Means of the Study’s Variables (Self-Efficacy and Task Value) in
Relation to Task Choice for each of the Three Scenarios ………………. 43
Table 12. T-Test for Equality of Means between Choosers and Non-choosers for
each of the Study’s Scenarios …………………………………………… 48

x
Abstract

The improvement of human performance is the goal of individuals and organizations that
contribute to that endeavor in the workplace. Decisions are made to suggest training as an
intervention when there is no evidence supporting a lack of knowledge and skills in the
target population. Training being offered as the only solution is often due to a lack of
confidence in or value for alternative performance improvement interventions. Thus, this
study involves those people who decide what interventions to apply to improve human
performance.
This study investigated the characteristics (self-efficacy, task value, and task choice) that
human performance professionals have as they consider solving motivation problems in
the workplace. The data collection instrument used to measure these characteristics was a
web-based questionnaire survey. The instrument presented a scenario for each type of
class performance improvement problem: i.e., lack of knowledge / skills, lack of work
motivation, and a performance-inhibiting work environment. At the end of these three
scenarios, the participant was asked if they would choose the work of solving the
performance problem. A group of 5,434 of human performance professionals were
invited to participate in the survey. From this group, 634 chose to take the survey, which
is 12%.
This study focused on the relationship that exists between self-efficacy and task value
with task choice. As predicted, the results of this study indicate that human performance
professionals who are given a performance problem scenario, and who possess high self-
efficacy and high-perceived task value will choose to solve the work performance

xi
problem. In the study, there was also a significant difference in self-efficacy and
perceived task value between those who chose to solve human performance problems and
those who chose not to solve the problem. Furthermore, the study indicated that human
performance professionals, when given a choice, more often chose to solve knowledge /
skill problems (89%) rather than either work motivation (77%) or work environment
problems (64%).
The practical aspect of this study showed that a significant number of individuals did not
choose to solve work motivation and work environment problems when such a choice
was appropriate. This finding suggests that the human performance professional give
more emphasis (and / or time) on learning how and why these types of human
performance problems could be solved.

1
I. PURPOSE OF THE STUDY
Introduction
The purpose of this study was to investigate the factors (namely, self-efficacy and
task value) that are associated with task choice. This study is situated within the context
of Human Performance Technology (HPT) professionals’ choice to solve work
motivation problems. These individuals considered the choice of solving three different
human performance problems that were described in three workplace scenarios. They
also answered questionnaire survey items that indicated their self-efficacy and value
beliefs regarding their motivation to solve these human performance problems. The
respondents to the survey were human performance professionals who volunteered to
take the survey or members of at least one of the following HPT professional societies:
International Society for Performance Improvement (ISPI), American Society for
Training and Development (ASTD), and the Society for Industrial and Organizational
Psychology (SIOP). The total membership of these three organizations is approximately
86,000 persons. A total of 5,434 individuals were selected from them to receive an e-
mail invitation to participate in the study (see Appendix A). ISPI, as one of the sponsors
of this study, contributed 510 email addresses, randomly selected from their member
database. ASTD has a members-only area on their website where the member directory
is accessible. Here, 144 email addresses were randomly obtained. Lastly, SIOP has a
direct link to member's email addresses from their website for a large contribution of
4,645 email addresses. An additional 135 email addresses were obtained from an HPT

2
professional LISTSERV. Here when an email is addressed to the LISTSERV mailing
list, it is automatically broadcast to everyone on the list.
The context of the study is situated around the work that human performance
professionals do to improve human performance in the workplace. This study
investigated the self-reported characteristics that human performance professionals have
as they consider solving motivation problems in the workplace.

Importance of the Study
The improvement of human performance is the ultimate goal of all individuals
and organizations that contribute to that human endeavor in the workplace. The term
improvement suggests that there is both a current, known level of performance, and a
desired one. This is the classic definition of a problem, i.e., a discrepancy between the
"should" and the "actual" states. Within the realm of inanimate objects, problems
normally occur during a performance cycle when a physical part breaks (the "actual").
You replace the malfunctioning part so that the machine can function according to the
manufacturer's specifications (the "should"). But what about human performance? What
are the "manufacturer's specification" for human beings doing work? Perhaps this is a
question for philosophers and religious studies. However, the performance of humans
and the specifications for that work performance is the domain of the human performance
professional.

3
The human performance professional is an individual who systematically looks
for barriers and inhibitors that prevent an individual from performing, or achieving an
intended goal. There are three class-problems associated with improving human
performance: individuals "cannot", "are prevented from", and / or "will not" perform
tasks to which they have been assigned (Clark, 1999). To create solutions that optimally
improve worker performance, the human performance practitioner must address all three
class-problems that exist. Matched to the three sources, or class-problems of "cannot",
"are prevented from", and "will not", there are three sets of interventions. Typical
examples of these interventions are training, environmental solutions, and motivation
solutions.
Many volumes have been written to address human performance problems at
work that are related to "cannot". Many of the current day's human performance
professionals have transitioned from the field of education and training. They are very
knowledgeable and skilled in “cannot” interventions. Other fields such as human factors
engineering, socio-technical systems, organizational development, process re-
engineering, and personnel management science are long term contributors in removing
work environmental factors, i.e. the “prevented from” interventions. By comparison,
there are relatively few models that address why an individuals "will not" perform (e.g.,
Ford, 1992; Maehr & Pintrich, 1997 ; Mager, 1991; Pintrich & Schunk, 1996).
Cries for help, such as, “How can I motivate these people?” and, “Why do people
do what they do?”, are currently being addressed by a number of human motivation
researchers in the work environment (Bandura, 1997; Clark, 1998, 1999; Franken, 1998;

4
Keller, 1999; Locke & Latham, 1990; O’Neil & Drillings, 1994; Smither, 1998).
Bandura (1997) indicates that high levels of worker motivation is essential for business
productivity. He further points out that accurate assessment of worker motivation
(especially, self-efficacy) by management is very important as an integral part of
appraising business opportunities and risks. Organizational goals will be difficult, if not
impossible to attain unless there is sufficient individual and collective motivation to
choose and persist at the goal. Diagnosing the causes of a motivation problem, (like low
worker self-efficacy) and implementing an effective remedy for that problem (like
reducing the workload) will reduce occupational stress and dysfunction. In fact, Bandura
(1997) suggests that those who have a low sense of efficacy suffer anxiety, health
problems, and health-impairing habits such as heavy drinking and sleep disturbances” (p.
464) which lower the overall productivity of the individual.
Human performance professionals need to quickly and accurately recognize that
there exists a motivation-related problem (diagnosis), construct an intervention which
removes this human performance barrier (prescription), and then provide evidence to the
stakeholders that the problem has been solved (evaluation). The skillful task of
diagnosing, prescribing, and evaluating problems related to human motivation is a core
competency for the human performance professional (Stolovitch, Keeps, & Rodrigue,
1995).
To further support the need for research in the work motivation area, The
Motivation Show (http://www.motivationshow.com/top.htm) conference organizers have
asked ISPI to participate in the year 2000 event. The Motivation Show organizers

5
requested ISPI’s participation due to a demand by their attendees that the conference
include research data to support both intrinsic and extrinsic motivational interventions.
Currently, the conference does not have any emphasis on intrinsic motivation factors and
very little data to support extrinsic motivational interventions.

Human performance professionals have also expressed their need to understand
the human performance problems that are caused by individuals who are not motivated to
accomplish their assigned work (Clark, 1998; Mager, 1991; Stolovitch, 1998). Since
motivation is not directly observable, it is necessary to use other observable behaviors
that allow the psychological researcher to ascertain whether motivation is present or not.
This study provides evidence that can be used to establish the credibility of a work
motivation model, like the one suggested in Figure 1.
SELF-EFFICACY & TASK VALUES
[are related to] TASK CHOICE

Figure 1. A Work Motivation Model

The term used in this study to identify the "will not" human performance
problem-set will be known as "motivation". The terms “work motivation” and
“motivation” are used interchangeably in this study.
Operational Definition of Terms
Most of the motivation research has been conducted in the environment that
focuses on children and college-age young adults (Pintrich & Schunk, 1996). In the

6
context where adults are primarily engaged in work tasks associated with organizational
goals, as opposed to academic tasks, such as learning, there are a few concepts that need
to adapted from educational research and operationally defined within the work
motivation context. The following concepts will be used throughout this study.
Human Performance
It is clear from business articles, such as A. T. Taylor’s Fortune magazine article
entitled, “GM's $11,000,000,000 turnaround” (1994), that focusing on the technology of
human performance is a very important and necessary strategy for increasing a
company’s global competitiveness and improving overall business profitability.
However, a question arises, “What is this Human Performance Technology (HPT), and
how can a competent business person contribute in this professional endeavor?” As an
initial answer to this question, HPT is briefly described in terms of: (1) the work the HPT
professional does – improving human performance; (2) the predominant characteristic of
the HPT worker – performance consulting; and (3) the fundamental tools and process the
HPT practitioner uses in the workplace (Rosenberg, 1998).
Performance Intervention
Harless defines interventions as “specific changes put into place to influence
human performance” (Dean & Ripley, 1998, p. 197). Companies implement changes in
response to a combination of factors: business needs, poor productivity, poor employee
moral, the availability of new technology, and social pressure. Most interventions are

7
initiated in response to a major event or real need, and some are implemented because of
the influence of a strong, popular personality (Hale, 1998).
A valid and reliable change in human performance is the mandate performance
interventions are required to produce. Langdon, Whiteside, and McKenna (1999) expand
the definition of an intervention to include the interactive environment of the individual.
They assert that an intervention is “any means used to bring about a change in
performance in an individual, work group, process, or business unit with the expressed
purpose of establishing, improving, maintaining, or extinguishing the performance from
an existing to a more desirable state” (p. 2).
Work Motivation
Pintrich and Schunk (1996) have provided a short definition of motivation,
"Motivation is a process whereby goal-directed activity is instigated and sustained" (p. 4).
They emphasize that motivation is a process rather than a product, in that we do not
observe motivation directly, but rather infer it from the results of other human behaviors,
such as, task choice, persistent commitment, expended effort, and self-reports (e.g., "I can
do this.", "I am prevented from doing this.", and / or "I will not do this.").
Smither (1998) defines work motivation as “the force that moves people to
perform their jobs” (p. 204). He indicates that tremendous amount time and money have
been expended in this area of work psychology. However, he argues that the results of
this research have not moved into industrial psychology practice with any consistency.
The reason for his suggestion, is that while most academic researchers focus on

8
identifying the causes of motivation, the majority of employers are usually interested in
what improves performance. This gulf that exists between research and applied
technology has been recently articulated in an article by Clark and Estes (1999). They
indicate that time and money is often wasted (at best) or harmful effects happen when
using unsubstantiated craft-based solutions to solve work problems. They challenge the
business leaders to demand scientifically substantiated solutions be technically applied by
the human performance professionals in the pursuit of improved performance.
Research Hypotheses
This study proposed six hypotheses. The first two hypotheses are graphically
depicted in Figure 2. Hypothesis one states: for those individuals who choose to solve
human performance problems in the work place, their self-reported self-efficacy for a
scenario-based task will be higher than those who do not choose the task. Hypothesis
two states: for those individuals who choose to solve human performance problems in the
work place, perceived task value for a scenario-based task will be higher than those who
do not choose the task.
Task Choice
Yes No
Self-Efficacy HIGH LOW
Task Value HIGH LOW

Figure 2. Research Hypothesis Matrix

9
This study also proposed two hypotheses about the relationship of the study’s
variables. Hypothesis three and four asserts that individuals who self report high self-
efficacy (3) and high task value (4) will show a statistically positive correlation with a
positive task choice to solve human performance problems in a work setting. Likewise,
hypothesis five suggests that there is a positive relationship between self-efficacy and
task value for those same individuals (see Figure 3).
Self-Efficacy
Task Value
Task Choice:
Solve Work
Motivation
Problems
+ r
+ r
+ r
Figure 3. Relationship Diagram of the Study Hypothesis
The study further hypothesized (hypothesis six) that there would be more
individuals who choose to solve lack of knowledge and skills human performance
problems than either work motivation or work environment problems. The results of this
part of the study are intended to provide a basis to recommend that motivation problem

10
solving skills should be included in both academic and industrial / corporate training
programs for human performance professionals.
Organization of the Remainder of the Paper
The first section of this paper was developed to show that there is a compelling
need to provide new knowledge in the motivation research area. Specifically, the results
of the study are targeted at providing evidence that supports the hypotheses associated
with the study variables (i.e., self-efficacy, task value, and task choice) and to suggest a
possible intervention within the context of solving work motivation problems. Section
two is organized to reflect the literature’s perspective on motivation research, which
establishes the relevance of this study. From the literature, four motivation models are
reviewed and a summary of motivation research studies are provided as the theoretical
basis for selecting the constructs on which this study was based, namely, self-efficacy,
task value, and task choice. Section three describes the methodology that was used in
this study. A description of the instrument, data collection process, and analysis
procedures are covered. Section four presents the results of study’s data analysis.
Section five provides a summary of the results and the conclusions that were drawn from
of this study, and implications for further research in this area.

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II. MOTIVATION: A BRIEF REVIEW OF THE LITERATURE
In this section, the constructs of self-efficacy, task value, and task choice will be
reviewed within the work motivation and educational psychology literature. The results
of the search for relevant literature was consistent with the comments that have been
made by other motivation researchers, i.e., there is very little research that has been
published relative to these three constructs in the work motivation area (Clark, 1998;
Ford, 1992; Locke & Latham, 1994; Price & Blair, 1998). However, a large amount of
credible sources were found in the educational psychology research (Bandura, 1997;
Eccles & Wigfield, 1995; Pintrich & Schunk, 1996; Van Erde & Thierry, 1996; Weiner,
1992). The models of the above referenced motivation researchers have been adapted for
the purpose of this investigation. In the aggregate, these researchers subsume most of the
relevant and contemporary theories of motivation into a system comprised of self
perceptions of contextual abilities (self-efficacy) and value beliefs (task values), both of
which are related to task choice (Pintrich & Schunk, 1996). A fundamental assumption
that has been made in this study is that the findings from the research in educational
psychology (either for children or young college age adults) will be applicable for
comparison within the context of adult work motivation.
To support the work of the human performance professional’s need to understand
human motivation problems, a research driven technology model has developed by
Richard E. Clark of the University of Southern California (Clark, 1998, 1999). Clark has
leveraged the work of other motivation researchers, such as Pintrich and Schunk (1996),
to propose an adult work motivation model. The following sections describe the

12
motivation theories argued by Pintrich, Vroom, Ford, Bandura, and Clark. Based on the
foundation of these research summaries, a methodological strategy was executed with the
purpose of investigating the relationship that exists between three motivation variables,
specifically: self-efficacy, task value, and task choice.
The practical ramification of this study is presented in section five of this paper.
This is where the significance of the relationship between the motivation variables is
coupled with the analysis of the study’s data. The summary of the study indicates that
the population of HPT professionals will be more likely to choose to solve lack-of-
knowledge-and-skills performance problems and not choose to solve work motivation
problems. Furthermore, the implications are that interventions need to be put in place to
both enable (self-efficacy) as well as show the value for HPT professional’s choice of
solving work motivation problems.
Motivation Theories and Models
A theory constitutes a tentative explanation of a complex phenomenon and
sometimes predictions and generalizations such as the nature of learning or intelligence.
With the framework of major definitional components, several hypotheses may be
derived that serve to describe how each component can be manifested in a number of
different interrelated ways, depending on the characteristics of the group involved and the
nature of the tasks represented. However, as important as theory is, it is a gross blunder
to demand that “one needs a theory of learning in order to evaluate teaching” argues
Scriven (1991, p. 360). For example, one does not need to know about car mechanics to

13
evaluate driving. However, theory does provide structure for how and what a person
thinks about a complex subject (Rossi & Freeman, 1993). Thus, it is necessary, but not
sufficient that a theoretical foundation be established to conduct a study where the object
under investigation can not be observed. The sufficiency will be satisfied as the theory is
tested within a context of a given situation and population.
Rossi, Freeman, and Lipsey (1999) emphasize that motivation is a process rather
than a product, in that we do not observe motivation directly, but rather infer it from the
results of other human behaviors, such as, task choices, expended effort, persistent
commitment, and self-reports (e.g., "I cannot do this.", "I am prevented from doing
this.", and / or "I will not do this."). Thus they focus on motivation as an "instigated and
sustained mental activity which used cognitive strategies that individuals believe will
promote accomplishment of desired goals" (p. 4).
The following sections of this paper provide a brief description of current theories
that relate to work motivation. These theories provide the foundation that supports the
methodology that was used to conduct this study.
Value-Expectancy Theory

Expectancy theory asserts that both extrinsic and intrinsic rewards are needed to
sustain motivation. This theory was introduced by Vroom (1964, 1995). Vroom
indicated that an individual will not expend effort if they do not believe that: (a)
expended effort will lead to an accomplishment (expectancy); (b) performance will lead
to rewards (instrumentality); and (c) there is value in the outcomes of the performance

14
(valence). Essentially, if there is no expenctancy of success, rewards, or other value,
there will be no performance (Locke & Latham, 1990; Mager, 1991). Expectancy theory
has been used to predict several variables (specifically: satisfaction, performance, and
effort) that are related to adult work (Walker & Symons, 1997). A meta-analysis was
conducted by Van Erde and Thierry (1996) that substantiates the validity of Vroom’s
expectancy theory.
Motivational Systems Theory (MST)

Motivational Systems Theory (MST) fundamentally supports the concept of
achievement in human performance. Ford (1992) defines achievement of a task as "the
attainment of a personally or socially valued goal in a particular context” (p. 66). In other
words a task value. Ford (1992) further elaborates on his Motivational Systems Theory
"as the organized patterning of three psychological functions that serve to direct,
energize, and regulate goal-directed activity: personal goals, emotional arousal processes,
and personal agency beliefs" (p.3). According to Ford (1992) personal agency beliefs
are “anticipatory evaluations (i.e., expectancies) about whether one can achieve a goal;
including: (a) Capability Beliefs – expectancies about whether on has the personal
capabilities needed for effective action …; and Context Beliefs – expectancies about
whether the environment will be responsive to one’s goal-attainment efforts” (p. 45). In
order to provide lexical unification of other research, Ford (1992) suggests that his
definition of personal agency beliefs are similar to Bandura’s (1982) construct of self-
efficacy. Ford symbolically represents his motivation model as a formula (see Figure 4).

15
Motivation = Goals X Emotions X Personal Agency Beliefs
Figure 4. Motivation System Theory (Ford, 1992, p. 78)

Ford includes and excludes a number of variables in his three-factor model.
“Goals” he defines in the context of directive cognitions that can be personally evaluated.
Ford indicates that Vroom’s (1995) construct of valance (or the process of assessing
personally perceived value to an activity or task that leads to an achievement). Ford's
construct, “emotion”, contains both emotional arousal processes and non-emotional
affective states, such as pain and fatigue. Personal agency beliefs are defined in terms of
capability and context beliefs. He calls these beliefs, “regulatory feedforward
cognitions” (p. 79).
Self-efficacy Theory
Self-efficacy theory is one of the cornerstones in current motivation theory. It
focuses on the individual's perceptions about whether increased effort will result in the
desired outcomes (confidence). It also takes into account the feeling of competence and
effectiveness (Bandura, 1982). Bandura (1986) subsumes the self-efficacy concept in his
social-cognitive theory. He indicates that self-efficacy is similar to, but broader in
meaning than the construct of motivational expectancy.
Pintrich and Schunk (1996) define self-efficacy in terms of a task-specific, self-
concept, and self-perception of one’s competence. However, they distinguish self-
efficacy from self-concept (personality associated expectancy-value theory) and self-
competence (developmental psychology associated perceptions-of-competence research).

16
They suggest that the quintessential factor relevant to self-efficacy is that the individual’s
judgments of efficacy are always with respect to the cognizant goal of the task being
considered. Pintrich and Schunk are in agreement with Ford, in that they all believe that
self-efficacy is very, if not exclusively contextually based.
Self-efficacy theory also recognizes that outcome expectations are an integral part
of motivational behavior. The relationship that exists between an individual’s choice to
perform a task and the belief that that course of action will accomplish the goal are
central to the idea of outcome expectancy. Thus, the concept of task difficulty is
subsumed as a factor in the self-assessment of personal efficacy, i.e., given the perceived
difficulty of the task and my capacity to execute the task, can I accomplish the goal of the
task. If I can, I will choose the task (Cantwell, 1997; Eccles & Wigfield, 1995; Nicholls,
1984).
CANE Model
The description of an integrated human motivation model may lead the causal
observer to believe that the conclusion that Walker and Quinn (1996) made regarding the
leading theories related to human motivation has now been made obsolete. Based on
their review of literature, they concluded that even though there are five major themes
related to human motivation (i.e., (1) Competence, (2) Autonomy / Control, (3) Goal
Setting, (4) Feedback, and (5) Social Affirmations), there is no interdependent and
relational model which adequately integrates all of the motivationally related variables
into a system. Ford (1992) supports this view. He states that there is no unified meaning
for the term “motivation”. In fact, he tabulates 32 different theory clusters of motivation,

17
supported by over 100 motivation researchers (see 173-200). Pintrich (1991) also agrees
with this viewpoint. Pintrich (1991) makes a very strong statement regarding the status
of today’s motivation research: “one of the most important issues for the future viability
of the field of motivational theory and research is the theoretical and definitional clarity
of the constructs” (p.200).
In their review of motivational literature, Walker and Quinn (1996) suggest a very
clear mandate for the human performance technology community. They argue that there
is a great need for an interdependent and relational model, which adequately integrates
the entire essentials of motivationally associated variables into a comprehensive system
that explains how and why people choose to behave in a certain way. The construction of
such a robust model of motivation is a critical element if the human performance
professional is to answer the question, "Why do people do what they do?".
Clark (1998, 1999) has constructed a model that describes the variables related to
work motivation. He asserts that motivation is a two-stage process. Stage one (Figure 4)
focuses on commitment. Stage two of the model addresses mental effort.
PERSONAL
AGENCY
X EMOTION X VALUES = COMMITMENT
Efficacy (Can I do it?) (Do I feel like it?) Utility (Value later?)

Context (Barriers?) Interest (Am I curious?)

Importance (Is this me?)

Figure 4. CANE Model of Factors Influencing Task Commitment (Clark, 1999, p. 20)
The first stage of the CANE model results in a choice to commit to a task
intention. This task choice is made based on the individual's personal agency belief

18
assessment, i.e. "How confident am I that I can do this task?" and "Are there any context
that would inhibit me from accepting the task?” Clark (1999) suggests that the perceived
difficulty assessment is based on both the individual’s perceived ability and their
identification of inhibiting barriers within the context of the work. If the individual
assesses the task to be too difficult (whether the assessment is actually true or is a
misjudgment) the individual will choose not to continue. On the other hand, if the
individual is either partially or totally confident in his or her ability to accomplish the
task, the intention persists. The emotion or mood of the person is then self-assessed, i.e.,
"Do I feel like doing this task?" If the answer is negative, the process terminates. If
affirmative, the process continues with an assessment of whether the intended
achievement is personally valuable. The overall value assessment is a process of
evaluating the areas of utility (“Will I find this task to be useful later?”), interest (“Am I
curious about this area?”), and importance (“Is this what I like to do?”). Some examples
of these three value-related considerations include: (1) utility: "The consequence of
making this sale means that I will be paid one million dollars"; (2) interest: "I am really
engaged in finding out how to do this task”; and (3) importance: “I feel so good every
time I accomplish this task”. If the answer to any of the value assessment questions is
negative, the motivation process will likely cease. The choice or commitment to a task is
therefore made up of positive individual assessments of self-efficacy, current mood, and
value of the task itself in achieving the desired goal. The first stage of the CANE
motivation model is thus composed of three variables which are multiplicative in nature,

19
i.e., if there is an absence of any of the three, no matter how strong the other two
variables may be, there will be no task choice (Price & Blair, 1999).
Clark (1997, 1998, 1999), along with Vroom, (1995), Ford (1992), Bandura
(1997), and Pintrich and Schunk (1996) have all described motivation theory as a linkage
between an individuals perceptions of self-efficacy, task value, task choice, and the
persistent commitment of effort to accomplish the intended goal. They have also
recognized that these personal judgments are best thought of as continuums. However,
Bandura (1982) and Pintrich, Smith, Garcia, and McKeachie (1991) have suggested that a
simple high/low comparative measurement of these self-reported judgments do provide
an important insight into motivated behavior and affect.
Summary of Motivation Research Literature
The majority of human performance professionals do not invest their time
learning about motivation theory. Even fewer actually choose to solve motivation-related
performance problems. Many of them come from business domains, so they may not
know that there exists research in the area of human motivation that can assist them in
solving these types of problems. In addition to not knowing about current research in the
motivation area, the past business management approach to motivation has been to treat
what goes on inside the individual as a "black box”. In other words, management has
primarily focused on the external (extrinsic) aspects of why people behave the way they
do. This approach to managing people has created a mind set that the only way to
motivate workers is to provide incentives such as an increase in pay, benefits, or status.

20
The American Management Association is a powerful force in the world of work.
They have recently released a management briefing that was authored by Haasen and
Shea (1997) suggesting that industry still bases its efforts to improve worker motivation
on the theories advanced in the 1950s (Herzberg, Mausner, & Snyderman, 1959; Maslow,
1954; McClelland, 1953). Haasen and Shea (1997) suggest that the application of these
extrinsic based theories in the work environment may actually be detrimental to people’s
motivation. In their review of extrinsic and intrinsic motivational theories, Haasen and
Shea (1997) claim that intrinsic motivation theory better explains why people choose to
work than external theories of motivation. Research studies in motivation conducted by
Ryan and Deci (1996), Csikszentmihalyi (1990), and Bandura (1997), all provide
evidence that supports Haasen and Shea’s (1997) claim that intrinsic motivational factors
based on cognitive science better explains why people choose to work than does extrinsic
motivation theory.
As was just referenced, many professionals disagree on what affects motivation
and how motivation effects human performance. Pintrich and Schunk (1996) have
provided a short definition of motivation, "Motivation is a process whereby goal-directed
activity is instigated and sustained" (p. 4). The definition of motivation proposed by
Pintrich and Schunk (1996) is simple only on the surface. Underneath the theoretical
framework that makes up the construct of motivation there are a very complex and
difficult to measure set of variables (Bandura, 1997).
The literature has many instances of motivation models that include self-efficacy,
task value. A few include the task choice variable within their motivation model. The

21
vast majority of the studies are situated within the academic environment where academic
achievement is the desired outcome. For the purposes comparison to the results of this
study, the concepts of and results derived from achievement motivation and work
motivation research domains were be considered during each phase of this study. The
following section summarizes the arguments, rationale, and research results that relate to
this study.
Pintrich and DeGroot (1990) conducted a correlational study that examined self-
efficacy and task value. The concept of task value was subsumed in their construct of
intrinsic value. In a study of 173 seventh grade (United States) science and English
students, they found that these two variables were positively related to cognitive
engagement choice (in other words, choosing how and what to learn). As they predicted,
higher levels of self-efficacy (r = .33, N=173, p < .001) and intrinsic value (r = .63,
N=173, p < .001) were correlated with cognitive strategies choice.
Brookhart (1997) reviews the literature regarding student motivation within the
classroom assessment setting. Within Brookhart’s (1997) model of theoretical
motivation framework, she suggests that the individual’s perceive task characteristics of
importance, utility, interest, together with their perceived self-efficacy will affect the
amount of mental effort the individual will expect to spend. Thus, these two variables of
task value and self-efficacy are moderating variables as the student assesses how much
effort the task will take to accomplish.

22
Two other studies (Wang, 1997; Lin, 1999) also investigate motivation constructs
within the academic environment. Wang (1997) and Lin (1999) both argue that self-
efficacy and task value affect the commitment (or sustained choice) that an individual
will make. Both of these studies predicted and confirmed that higher self-efficacy and
task value the students had the stronger commitment they made to the task of learning.
Eccles and Wigfield (1995) assessed the dimensionality of and relations between
the factors related to task value, namely interest, perceived importance, and perceived
utility. They also assessed the two factors related to self-efficacy (i.e., expectancies for
success and ability perceptions). Both exploratory factor analyses and confirmatory
factor analyses indicated that all of the factors should be included in the structured
equation model (all of these factors had a loading greater than .70 and the GFI was
substantiated). They also found that task values and self-efficacy were positively related
to each other.
Feather (1988) conducted a study to test hypotheses that related to the enrollment
decisions of 444 university students. He found that the task choice of students who are
considering an enrollment in mathematics and English courses was related to the value
that students placed on the context of the courses and how efficacious they felt about
doing well in the course. Feather’s (1988) study most closely parallels this study, except
that Feather’s context is associated with students making a choice of which academic
course they would choose, and the study this paper describes looks at individuals who
choose to solve work performance problems. Both of these studies take a general
approach of analyzing human behavior that is linked with self-belief concepts. Feather

23
(1988) argues that this approach is widely used in theoretical interpretations of job
selection, work performance and satisfaction, and in models of human decision making.
Feather (1988) used the Rokeach Value Survey as the measuring instrument to
investigate the motivational factors associated with college course enrollment choice (N
= 44). Feather used two items for each course choice (mathematics and English) to
measure self-efficacy (mathematics: α = .77; English: α = .80). Three items were used to
measure the value (i.e., interest, utility, and importance) students placed on the
mathematics and English course enrollment choice. The internal reliability (coefficient
alpha) for the English value items was .79. The internal reliability (coefficient alpha) for
the mathematics value items was .69.
In agreement with the other studies suggested here, Locke (1997) integrates
several decades of empirical research on motivation to formulate a model of work
motivation. Within his work motivation model, he argues that the value an individual
places on the job (or task), together with their self-efficacy judgement will affect their
approach or avoidance of a job. In other words, if an individual has high self-efficacy
and value the job, they will be more inclined to choose to perform within the context of
the job. He further indicates that task value affects self-efficacy as the individual
performs the job.
Motivation Constructs Selected for the Study
There are many constructs proposed to explain why an individual will choose to
perform work. For example, Eccles and Wigfield (1995) provide evidence in an

24
academic setting that there are three primary constructs related to task choice (i.e., self-
efficacy, task value, and task difficulty). Pintrich and Schunk (1996), Ford (1992), and
Clark (1998) agree with Eccles and Wigfield (1995) that self-efficacy and task value are
antecedents to task choice. However, Ford (1992) and Clark (1998) argue that task
difficulty is actually an antecedent to self-efficacy. Ford (1992) and Clark (1998) also
suggest that emotion is also an antecedent of task choice, but concede that the emotion
variable has much less impact than either self-efficacy and task value. Based on this
review of motivation research, this study chose to focused on the relationship that exists
between three motivation constructs in adult work motivation research: self-efficacy, task
value, and task choice.
Self-efficacy
Perceived self-efficacy is defined as, "People's judgments of their capabilities to
organize and execute courses of action required to attain designated types of
performances" (Bandura, 1986, p. 391). For example, self-efficacy can predict outcomes
such as academic achievement, pain tolerance, and athletic performance (Bandura, 1986;
Schunk, 1991). In discussing the role of self-efficacy in the workplace, Bandura (1997)
indicates that perceived efficacy affects both the considerations of the task choice
options, and the information that is collected to make the task decision.
Self-efficacy can be described as the beliefs that "influence how people feel,
think, motivate themselves, and behave" (Bandura 1993, p.118 ) As Bandura (1993)
suggests, it is one thing for an individual to possess the necessary knowledge and skills to
perform a given task, and quite another to embody the self-beliefs to execute those tasks

25
under arduous conditions. Consequently, an individual with the same knowledge and
skills may perform poorly, satisfactory, or extraordinary depending upon the fluctuations
in self-efficacious thinking (Bandura, 1986, 1993).
Task Value
The concept of “task value” embodies the idea that individuals will be more or
less motivated to choose a task based on their interest in the content of the task, their
personal assessment of the utility of the task (i.e., the ability of the task chosen to
accomplish the goal), or the intrinsic importance the individual places on doing well on
the task (Eccles & Wigfield, 1995; Pintrich & Schunk, 1996; Van Erde & Thierry, 1996;
Wigfield & Eccles, 1992).
The relationship of self-efficacy, task value, and task choice was investigated in a
study conducted by Feather (1988). His study provided evidence that demonstrated a
positive correlation between students who possessed a higher math self-efficacy, higher
task value for math, and their task choice to enroll in a science course.
Task Choice
The act of choosing a specific course of action in order to accomplish a given goal
is the definition this study will use to describe task choice. In further defining this
construct, Pintrich and Schunk (1996) indicate that the two most important predictors of
task choice are self-efficacy and task value. In predicting task choice, the self-assessment
of efficacy and perceived task value can be colloquially stated in terms of: “Am I able to
do this task?” (self-efficacy) and, “Why should I do this task?” (task value) (Wigfield &
Eccles, 1992).

26
Bandura (1997) argues that self-efficacy affects an individuals choice of tasks,
effort, and persistence. Those individuals possessing low self-efficacy for accomplishing
a task will not choose it. Conversely, those who believe they have the capability to
perform the task will choose the task. Pintrich and Schunk (1996) suggest that
individuals acquire information in order to assess their situationally-specific efficacy
from their past performance results when they chose a similar task, vicarious experiences
from credible sources, persuasive suggestions, and current physiological condition.

27
III. METHOD AND PROCEDURES OF THE STUDY
This correlational study was planned to investigate the relationship between three
constructs: self-efficacy, task value, and task choice. To measure the three variables, a
questionnaire survey was constructed. Details of the questionnaire will be discussed in
the Measures and Instrument section of this chapter. An item analysis was performed
using the data collected from the questionnaire survey to establish the reliability of the
instrument. A correlation analysis was conducted to test the null hypotheses that there
are no relationships between the study variables. Descriptive data also are provided
about the target population sample to support the assertion that the sample’s study results
can be generalized to the total population of human performance professionals.
The following sections describes the study’s theoretical framework,
methodological design, survey questionnaire respondents (participants), the measures and
instruments that were used in this study, and the field procedure that was implemented to
collect the data. The results of this methodological approach was intended to provide
evidence to support the assertion that there exists a relationship between self-efficacy,
task value, and task choice in the context of an HPT professional’s work of solving
human performance problems. These results will also provide evidence to substantiate
phase one of Clark’s (1998, 1999) CANE motivation model.
Theoretical Framework for the Study
The methodology this study used was targeted at certain aspects of Clark’s (1997,
1998, 1999) CANE motivation model. For the purposes of this study, certain phase one

28
components of the CANE model were considered. This study investigated four of the
eight phase one components suggested by the CANE model. The bolded components in
Figure # show the components that were under investigation in this study.
PERSONAL AGENCY X EMOTION X VALUES = COMMITMENT
Efficacy (Can I do it?) (Do I feel like it?) Utility (Value later?) Choice (Shall I do it?)
Context (Barriers?) Interest (Am I curious?) Persistence (Am I distracted?)
Importance (Is this me?)
Figure #. The Four CANE Model Components Under Investigation in this Study
Self-efficacy (“Efficacy”) is a key factor for the individual who is considering
making a commitment to choose a task (Price & Blair, 1998). Clark (1999) indicates that
if an individual possesses high self-efficacy, it will contribute to that individual’s
commitment to a task. This component of the CANE model was assessed in the study by
using two questionnaire items contained in the survey (see the Design section below).
The context in which an individual chooses to work may have barriers that inhibit
his or her performance. Within the framework of this study, context barriers are not
under investigation. There are no explicit barriers stated in the scenarios. Likewise, the
emotion factor is not investigated, since there is no reference in the scenarios to how the
individual is feeling at the time they were to make their task choice.
Clark (1999) defines the “values” factor (labeled task value in this study) in terms
of three components, i.e., “Utility”, “Interest”, and “Importance”. Clark (1999) further
emphasizes the importance of the value factor in that he asserts that this task value factor

29
is an essential characteristic in any motivated behavior. Like the self-efficacy study
variable, the task value variable was measured using two questionnaire items (one item
related to the utility component and one item related to the interest component). This
study chose to use two of the three CANE model components, namely, “Utility” and
“Interest”, to measure task value.
The self-efficacy and task value variables were the independent variables in this
study. These variables were associated with the choice that an individual would make to
engage in a task that was suggested in a work related scenario. Clark (1997) indicates that
there are two components that comprise commitment, i.e., choice and persistence. In this
study, the component of choice was investigated. The task choice variable was measured
by asking the survey participants to answer either “Yes” or “No” to questions that asked
if they would choose to solve specific human performance problems.
Thus, four components of the CANE model (specifically: efficacy, utility,
interest, and choice) were used as the theoretical framework to underpin a methodology
designed to investigation three variables: self-efficacy (efficacy), task value (utility and
interest), and task choice (choice).
Design
The study’s correlational design included deriving descriptive statistics for the
target sample. An item analysis statistics was also done for the two motivation constructs
that were represented by the questionnaire’s item sub-scales, namely, self-efficacy and
task value. Each questionnaire item included in the survey was derived by modifying

30
two items from each of the sub-scales (self-efficacy and task value) listed in Pintrich,
Smith, Garcia, and McKeachie’s (1991) Motivated Strategies for Learning Questionnaire
(MSLQ). Demographic information questions, and three scenario-based (yes / no) task
choice decisions made up the questionnaire survey.
Participants
The group (N=5,434) of human performance professionals that were invited (see
Appendix A) to take the survey were given 14 days to respond to the invitation. To
generate sufficient credibility for this study, University of Southern California, and ISPI
were prominently positioned in the invitation. The USC human subjects procedures were
followed.
For the purposes of this study, a human performance professional was defined as
anyone who was either working in a human performance related job or was associated
with one or more HPT oriented professional society. A LISTSERV provided access to
those working in a human performance related job, such as training, performance
consulting, or human resources. A LISTSERV is an automatic mailing list server. When
e-mail is addressed to a LISTSERV mailing list, it is automatically broadcast to everyone
on the list. The International Society for Performance Improvement (ISPI), the American
Society for Training and Development (ASTD), and the Society for Industrial and
Organizational Psychology (SIOP) were the targeted professional societies from which
the sample was taken. By targeting these professional societies and the LISTSERV, a
total of 5,434 invitations to participate in the study were sent out (see Table 1).

31
Table 1
Invitation to Participate in the Study, by Professional Organization
Source N
Responded
%
ASTD 1441913%
ISPI 51012725%
SIOP 4,64544410%
Other HPT Professionals 135 44 33%
Total 5,43463412%

A group of 634 (12%) individuals positively responded to the e-mail invitation
and thus, selected themselves to answer the questionnaire survey. Of the 634 surveys
returned, 16 were not included in the data analysis due to incomplete data. Thus, data
analysis was conducted on the data collected from 618 participants.
The respondents were told that the survey would only take 10 minutes. They
were also instructed to provide their name and e-mail address so that they could receive
the results of this study. In order to keep the amount of time an individual would have to
spend taking the survey to 10 minutes, there were only five demographic questions asked
(see Table 2).

32
Table 2
Demographic Data Requested From Survey Respondents
Demographic Item Expected Response
Name first and last names
E-mail e-mail address
Years of Total Work Experience #
Years Practicing Human Performance
improvement
#
Professional Society Membership Select as many as apply:
ASTD
ISPI
SIOP
Other

The sample who took the questionnaire survey was made up of working adults
with work experience ranging from 0–60 years of work experience and 0–60 years of
human performance consulting experience. The mean work experience was 18 years and
the mean human performance consulting experience was 10 years (see Table 3).

Table 3
Survey Participants’ Work Experience
Demographic Item N Mean
Standard
Deviation
Years of Total Work Experience 618 17.93 11.31
Years Practicing HPI 618 10.05 9.22

33
Table 4 shows the participants’ demographic information related to their
association with zero or more HPT professional societies. Only 21 participants responded
by selecting no professional society affiliation.
Table 4
Survey Participants’ Professional Society Membership
Professional Society Membership
Count
n=618

ASTD selected 165
ISPI selected 218
SIOP selected 358
Other selected 251
Nothing selected 21
Other selected (and not selected:
ASTD, ISPI, & SIOP)
16

Two memberships selected 250
Three memberships selected 56
Four memberships selected 11

Measures and the Instrument
The survey was designed to identify an individual’s self-efficacy and task value
for solving human performance related problems in the workplace. This questionnaire
survey was constructed by adapting the self-efficacy and task value items from Pintrich,

34
Smith, Garcia, and McKeachie’s (1991) Motivated Strategies for Learning Questionnaire
(MSLQ) (see Appendix B). The questionnaire contained a motivation questionnaire scale
comprised of two sub-scales (self-efficacy and task value), and a task choice
questionnaire item for each of three human performance work related scenarios. Since the
MSLQ was designed to measure academic achievement, the questions were changed to
reflect the human performance improvement problem-solving environment. However,
the intent of the questions was designed to remain the same as the original MSLQ items.
The survey looks at three distinct problem areas: knowledge and skills, work
motivation, and work environment. Each section starts with a scenario (see Figure 6),
which sets up a performance problem that a human performance consultant might be
asked to work on. The scenarios were composites of typical HPT projects. The first
question after the scenario (see Figure 7) asks the survey participants to indicate (by
selecting Yes or No) whether they would choose to solve the problem described. The
survey then queries the participants about their self-efficacy and task value associated
with solving the human performance problem described in the scenario. A four-point
Likert response system was used for the motivation scale’s questions. They were asked to
respond to the following options: (1) Not True; (2) Somewhat Not True; (3) Somewhat
True; (4) Very True. The MSLQ uses a seven-point Likert-scale. The intent in using the
four-point scale was to reduce the cognitive load on the participants. By restricting the
alternatives to an even number, this eliminated a neutral midpoint and enforced an
opinion (DeVellis, 1991). See Appendix C for the actual web survey.

35

----------------------Scenario 2: WORK MOTIVATION PROBLEM-----------------------

Your client, the Manager of Customer Relations, has just been informed that the
nursing staff is not completing Customer Service Forms (CSF). A complete performance
analysis has provided the following information:
• The lack of information from the completed CSFs costs the hospital nearly a million
dollars ($1,000,000.00) each year.
• The nurses have sufficient knowledge and skills to complete the CSFs.
• There are no obstacles in the work environment that would prevent the nurses from
completing the CSFs
• The nurses indicated that although they know they are responsible for completing the
CSFs, they choose to spend more time providing patient care rather than filling out
the paperwork.

Figure 6. Sample Scenario
The three scenarios included in the questionnaire, focused on a choice by the
human performance professional (the participant) to perform the work of solving three
class performance improvement problems: a lack of knowledge / skills problem, lack of
motivation problem, and a performance-inhibiting work environment problem. To ensure
that the instrument’s scenarios and context directed questions was valid, the final draft of
the survey questionnaire was sent to six human performance experts. They agreed that the
scenarios were valid for the purposes of this study. They also suggested minor
grammatical corrections that were incorporated into the final instrument. The final
survey questionnaire was delivered through the Internet of world-wide web, using the
facilities of the Saratoga Group.

36

Yes No

1. I would choose to solve this work motivation
performance problem.

Not
True
Somewhat
Not True
Somewhat
True
Very
True
2. I think solving work motivation performance problems
is valuable.

3. I am very interested in solving work motivation related
problems.

4. I believe I would be very successful if I implemented a
work motivation solution for the above scenario.

5. I am confident that I could do an excellent job in solving
the work motivation problem described in the above
scenario.

Figure 7. Sample Items
The study was conducted by the University of Southern California and was
sponsored by ISPI and the Saratoga Group. ISPI provided 510 members e-mail
addresses. The other 4,924 addresses were obtained from publicly available e-mail
membership lists, such as ASTD and SIOP. The Saratoga Group provided the web site
and questionnaire survey Application Service Provider (APS) services. Thus, the official
logos of the three supporting organizations were depicted on the questionnaire survey.
The quality of the questionnaire survey items on the survey were also judged
using commonly accepted questionnaire checklists and guidelines. For instance, the best
surveys have these features: specific objectives, straightforward questions, sound
research design, sound choice of population or sample, reliable and valid survey
instruments, appropriate analysis, accurate reporting of survey results, and reasonable

37
resources (Fink, 1995). The Saratoga Group provided the web-based delivery appearance
for the survey. The quality and clarity of the performance problem scenarios was
facilitated by a critical review to ensure the appropriateness and physical layout qualities
of the questionnaire are optimal.
Data Collection and Analysis Procedures
An invitation to take the web-based survey was given by sending an electronic
mail (e-mail) message to 5,434 human performance professionals. The invited
professionals were given 14 days to respond to take the survey. The International Society
for Performance Improvement (ISPI) provided a randomly selected sample of 510 e-mail
addresses from its membership list. The remainder of the e-mails were compiled from
publicly accessible e-mail lists from both the American Society of Training and
Development (ASTD) and the Society of Industrial and Organizational Psychology
(SIOP). The data related to individuals who did not complete all of the scenario question
items were removed. The resulting data from these 618 completed surveys were
automatically collected and extracted from the Saratoga Group’s web-based survey
facilities.
The self-selected respondents used their own access to the Internet to complete
the survey. In the e-mail invitation to participate in the survey, they were given a URL
(Uniform Resource Locator), a world wide web address that housed the survey. They
were told that this effort was conducted by the University of Southern California,
sponsored by ISPI, and web-hosted by the Saratoga Group. They were also told that their

38
investment of 10 minutes in completing the survey was intended to improve HPT
curricula and programs in academic institutions as well as industrial / corporate
organizations. Because they provided their names and e-mail addresses, they were
assured that their anonymity would be protected and that they would be provided access
to the results of this study.
It was estimated that the time tolerance level of the individuals who choose to
answer the questionnaire would be only 10–15 minutes (from the Welcome Page through
the completion of the process to completion of the survey questionnaire). With this 10-
minute time target, several modifications were made to the original survey. The number
of MSLQ modified questions were reduced from 14 to 4, for each of the three scenarios.
The scenarios were provided, with very clear indications of exactly what the human
performance problem was, i.e., lack of knowledge and skill, lack of motivation, or
debilitating work environment. Lastly, only three demographic items were included in the
survey.
Table 5 summarizes the estimates of time that it would take to administer the
questionnaire keeping within a 10–15 minute time limit.

39
Table 5
Questionnaire Administration Process
Minutes
Required
Process Description
5 Open and read e-mail (not included in the 10-minute time limit.)
1-3 Log into the web site (not included in the 10-minute time limit.)
2 Survey login process (name and e-mail)
1 Questionnaire process Welcome and Introduction
2 Demographic items (3 items)
5-10 Scenarios 1, 2, and 3 read and answer questions

Reliability and Item Analysis
A test of the internal consistency reliability (Cronbach’s α) for both the self-
efficacy and task value scales was performed. The results of the reliability test are shown
in Table 6. In three repeated assessments, the self-efficacy and task value scales show a
range of alpha coefficients (from .44 to .95). Standard rule-of-thumb in behavioral
science research for the alpha coefficient is .70, scales with alpha coefficients above .69
are considered to be reliable. The reason for low internal consistency reliability could be
due to the homogeneity (little variability) of the participants’ responses. This could be
improved by adding more items.

40
Table 6
Cronbach α for the Self-Efficacy and Task Value Scales
Scenario Self-Efficacy Scale Task Value Scale
Knowledge/Skill Problem α = .44 α = .66
Work Motivation Problem α = .66 α = .92
Work Environment Problem α = .54 α = .95

Table 6 shows low alpha coefficients for the self-efficacy scale for each of the
three scenarios. This is a problem most probably due to the restriction of range from the
responses that the survey participants provided (see Table 7). The participants were
selected because they are performance consultants. Human performance consulting has
as one of its roots, training. It is highly likely that the selection of participants would be
efficacious in working on knowledge/skill problems. This could explain why the self-
efficacy variable for knowledge/skill is the low.
The item analysis results are lower than other research in this area (Blair, O’Neil,
& Price, 1999; Feather, 1988; Mulkey & O’Neil, 1999). It should be noted here that all
questions related to both the self-efficacy and task value variables were modified due to
the context of the particular study.
The survey was available to the respondents from February 19, 2000 until March
3, 2000 (14 days). Experience and advice from the critical review team (HPT experts)
suggested that a short time frame to respond would result in greater number of
respondents. The respondents were instructed to send an email to the Principal

41
Investigator from the University of Southern California if they had any questions
regarding the survey or the process of the study. Due to a large number of participants
interested in taking the online survey, a bottleneck was created on the computer where
the survey resided. This traffic jam was cleared up by the following day. So the initial
surge of email comments was due to inability to access the survey. Many sent an email
to notify of their not being able to get online, they asked if there was another way of
obtaining the survey. Several people returned questions with their concerns such as
asking how their email address was made available, requesting that their email address be
removed from the list, and questioning the authenticity of the whole process. Lastly,
about ten people asked to receive the results of the results when the data collections was
complete. The completed surveys were automatically coded and entered into a data file
by the Saratoga Group’s web-based survey application. The correlation analysis results
are reported in the next section.

42
IV. RESULTS OF THE STUDY
Descriptive Statistics
The means and standard deviations for the items associated with the variables in
this study are portrayed in Table 7. There were three human performance improvement
work scenarios provided as context to the survey participants, i.e., knowledge / skill
problem (K), work motivation problem (M), and work environment problem (E). The
study variables were coded with the constructs (i.e., SE = self-efficacy; and TV = task
value) listed first and the associated scenario context listed second. Thus, as shown in
Table 7, the first study variable results listed in the table would be linked to the self-
efficacy construct contained in the knowledge / skill problem scenario (SE-K).
Table 7
Means and Standard Deviation of Self-Efficacy and Task Value Items for each Scenario

Study Variables Mean
Standard
Deviation
N
Knowledge/Skills 7.28 1.00 618
Motivation 7.10 1.15 618
Self-Efficacy
Environment 6.97 1.18 618
Knowledge/Skills 6.57 1.32 618
Motivation 5.91 1.67 618
Task Value
Environment 5.68 1.84 618

The two-item composites for the self-efficacy item responses in each scenario
showed a means that were higher than the means of the two-item composites for task

43
value. In the study, the possible range for the composite for the two constructs (self-
efficacy and task value) was 2.00 (minimum) and 8.00 (maximum). For each of the two
constructs, a composite result of 2.00 would indicate that the survey participants self-
reported that they did not feel confident to solve the human performance problem or that
they did not value the work itself. A composite result of 8.00 would indicate that the
survey participants self-reported that they did feel confident to solve the human
performance problem and that they did value the work.
The means of the composite study variables were between 5.68 and 7.28. This
indicates that the survey participants self-reported a high amount of self-efficacy and task
value, regardless of the scenario. Self-efficacy and task value means were highest for the
knowledge / skills problem scenario. The field of human performance consulting has as
one of its foundations, training. This would explain why many of the human performance
professionals surveyed selected to work on knowledge / skill problems.
The t-test for statistical significance, which is used to show the difference
between self-efficacy and task value, was generated for each of the three scenarios. The
fact is, these scores are not independent since each participant has been measured more
than once. The repeated measures multivariate analysis of variance (MANOVA) takes
this into account. The repeated measures MANOVA makes it possible to separate out the
effect of individual differences from the effect of the variable that serves to differentiate
the groups. The F ratios (18.484 and 71.689) in Table 8 shows there is a significant
difference between the measures of self-efficacy and task value factors. So it is
improbable that the means represent random samples from this same population. The

44
Wilks’ Lambda indicates whether self-efficacy and task value contributes significantly to
explaining additional variance in task choice. The values (λ=.943 and λ= .811) are both
high, which says that the variance is explained by factors other than the difference
between the self-efficacy and task value means.
Table 8
Repeated Measures Multivariate Analysis of Variance (MANOVA)
Effect Wilks' Lambda
Value
F Sig.
Self-Efficacy .943 18.484 .000
Task Value .811 71.689 .000

Table 9, shows that more individuals chose to solve knowledge / skill human
performance problems than either work motivation or work environment problems. This
supports the study’s sixth hypothesis regarding the task choice of solving human
performance problems in the workplace, i.e., the population of human performance
professionals will more often choose to solve knowledge / skill performance problems in
comparison to the choice of solving work motivation or work environment performance
problems.

45
Table 9
Frequency of the Task Choice to Solve Human Performance Class Problems (N = 618)
Knowledge / Skill
Problem
Work Motivation
Problem
Work Environment
Problem
Task Choice
count % count % count %
Yes 553 89 477 77 393 64
No 65 11 141 23 225 36

Correlational Analysis
A correlation analysis shows that there is a positive correlation between the self-
efficacy assessment in scenario 1 and scenario 2 (r=.114). Scenario 2 and 3 self-efficacy
assessments are significantly correlated (r=.354). Likewise, scenario 1 and 3 self-efficacy
assessments were significantly correlated (r=.352) The task value variable assessment
shows similar correlation results (r=.194, r=.312, and r=.270) (see Table 10). The
correlation analysis also shows divergent (discriminant) validity between self-efficacy
and task value because they do not correlate too closely together (Litwin, 1995). All but
two self-efficacy for knowledge / skill with task value for work motivation (r=.019) and
self-efficacy for knowledge / skill with task value for work environment (r=.074) of the
bivariate correlations (Pearson r) were significant to the 0.01 level. The low questionnaire
item Cronbach alphas values are mitigated with this analysis (see Table 6). Thus, the
data shows an indication that there is consistency across the three scenarios with respect
to the respondent’s self-report of self-efficacy and task value for choosing to solve these
human performance problems.

46
Table 10
Pearson Product-Moment Correlation for the Self-Efficacy and Task Value Scales within
each of the three Human Performance Improvement Scenarios (N=618)

SE_K SE_M SE_E TV_K TV_M TV_E
Self-Efficacy Knowledge
/Skills
Pearson
Correlation
1.000 .114 .352 .382 .019 .074
Sig. (2-tailed) .005 .000 .000 .642 .065
Motivation Pearson
Correlation
1.000 .354 .107 .527 .125
Sig. (2-tailed) .000 .008 .000 .002
Environment Pearson
Correlation
1.000 .202 .263 .502
Sig. (2-tailed) .000 .000 .000
Task Value Knowledge
/Skills
Pearson
Correlation
1.000 .194 .270
Sig. (2-tailed) .000 .000
Motivation Pearson
Correlation
1.000 .312
Sig. (2-tailed) .000
Environment Pearson
Correlation
1.000
Sig. (2-tailed)
Correlation is significant at the 0.01 level (2-tailed).

The correlation analysis supports the hypothesis that, within this study’s context,
the motivation constructs of self-efficacy and task value are related to each other. Results
of the correlation analysis (Table 10) indicated the stability of the self-efficacy and task
value variables across the three scenarios.

47
Table 11
Means of the Study’s Variables (Self-Efficacy and Task Value) in Relation to Task
Choice for each of the Three Scenarios

Item Composites
Task Choice N Mean Standard
Deviation
Self-Efficacy Knowledge/Skills
Choosers
Non-choosers
553
65
7.40
6.28
.85
1.45

Motivation
Choosers
Non-choosers
477
141
7.39
6.11
.82
1.49

Environment
Choosers
Non-choosers
393
225
7.36
6.31
.91
1.30

Task Value Knowledge/Skills
Choosers
Non-choosers
553
65
6.79
4.75
1.08
1.71

Motivation
Choosers
Non-choosers
477
141
6.43
4.19
1.29
1.66

Environment
Choosers
Non-choosers
393
225
6.64
4.01
1.18
1.58

The means of the two-item composite study variables that are related to the
participants’ task choice indicate that even though the participant chose not to solve the
human performance problem, they reported to have high self-efficacy and high task
value. However, the results of the t-test analysis (Table 12 indicated that there is a
statistical difference between each of the two groups.

48
When comparing the means of the study variables with respect to those who
chose to solve the human performance problem (“choosers”) and those who do not
choose to solve the problem (non-choosers), the results of the t-tests (as shown in Tables
11 and 12), indicate that across the three scenarios there is a statistically significant
difference between these two groups of people. Further supporting the original model
(see Figure 2) that those who choose a task will have higher self-efficacy and will value
the task more than those who do not choose the task.

49
Table 12
T-Test for Equality of Means between Choosers and Non-choosers for each of the
Study’s Scenarios

Levene's Test for
Equality of
Variances
t-test for Equality of Means

F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Equal variances assumed 54.84 .000 9.19 616 .000 1.12
SE_K
Equal variances not
assumed
6.13 69.30 .000 1.12

Equal variances assumed 33.55 .000 13.32 616 .000 2.03
TV_K
Equal variances not
assumed
9.35 70.13 .000 2.03

Equal variances assumed 91.90 .000 13.12 616 .000 1.27
SE_M
Equal variances not
assumed
9.74 166.14 .000 1.27

Equal variances assumed 7.63 .006 16.91 616 .000 2.24
TV_
M
Equal variances not
assumed
14.72 191.99 .000 2.24

Equal variances assumed 36.38 .000 11.74 616 .000 1.05
SE_E
Equal variances not
assumed
10.69 350.989 .000 1.05

Equal variances assumed 3.55 .060 23.47 616 .000 2.63
TV_E
Equal variances not
assumed
21.73 369.073 .000 2.63

The results from the equality of means t-tests (see Table 12) showed a very
significant difference between the study’s variables with regard to the choice the survey

50
participants indicated. These results supports the assumption that the choosers and the
non-choosers are different groups.

51
V. SUMMARY, CONCLUSIONS, AND IMPLICATIONS
Summary
The purpose of this study was to investigate three constructs (self-efficacy,
task value, and task choice) that are associated with human performance
professional’s choice to solve a human performance problem. The research on
motivation can be subsumed into a theoretical framework comprised of self
perceptions of contextual abilities (self-efficacy) and value beliefs related to task
choice (Clark, 1999; Pintrich & Schunk, 1996). However, the vast majority of this
research is situated within the context of academic achievement (e.g., Bandura, 1997;
Eccles & Wigfield, 1995; Feather, 1988; Garcia & Pintrich, 1994; Lin, 1999; Wang,
1997). The research this present study pursued was to validate this motivational
framework and compare the results of the current study to recent research from the
industrial context (e.g., Blair, O’Neil, & Price, 1999; Clark, 1999; Mulkey & O’Neil,
1999; Price & Blair, 1998).
As predicted, the results of this study indicate that human performance
professionals who are given an obvious performance problem scenario, and who
possess high self-efficacy and high-perceived task value will choose to solve the work
performance problem. On the other hand, human performance professionals who
have low self-efficacy and low perceived task value, relative to solving a work
performance problem, will indicate that they would not choose to solve the work
performance problem. The current motivation research literature (Bandura, 1997;
Blair, O’Neil, & Price, 1999; Clark, 1999; Mulkey & O’Neil, 1999) does show that

52
an individual who believes that they are capable (self-efficacy) of accomplishing a
task (solve work motivation problems) or values the task (Eccles & Wigfield, 1995;
Lin, 1999; Wang, 1997) will choose to perform work in this area. Our results support
this literature. The literature (Clark, 1999) also indicates that this relationship is
multiplicative. In other words, if there is an absence of either variable (i.e., self-
efficacy or task value), the individual will not choose the task.
The instrument used to measure the self-efficacy and task value constructs
was a survey questionnaire. There were 618 human performance professionals who
completed the survey. Two questions for each construct were used to measure the
self-concepts that the survey participants expressed in relation to a given human
performance problem scenario. This is similar to Feather’s (1988) methodology,
which also used two items to measure the self-efficacy of a student in choosing a
mathematics and an English class. Feather argued that two to three questions were
sufficient based on the high reliability coefficients (self-efficacy findings: α = .77
(mathematics context) and α = .66 (English context); task value findings: α = .69
(mathematics context) and α = .79 (English context)). This was in contrast with the
current study where the reliabilities for the two self-efficacy items were .44, .66, and
.54 for the three scenarios and the correlations for the two task value items were .66,
.92, and .95 for the three scenarios.
The questions used in this study were adapted from the Motivated Strategies
for Learning Questionnaire (MSLQ). Modifications made to the MSLQ questions

53
contextualized the questions to the content of three human performance problem
scenarios, namely, a knowledge / skill problem, a work motivation problem, and a
work environment problem. For each scenario, there were two questions adapted for
each construct. In addition, each participant was asked to make a binary decision of
whether they would choose to solve the human performance problem. Thus, for each
scenario, the survey participant answered five questions, for a total of 15 questions
for the entire questionnaire survey. The questionnaire items that were used in this
study were reliable and valid, at least for the purpose of establishing a relationship
between the three variables of this study. The reliability of the MSLQ’s self-efficacy
scale (8 items) is α = .93. For the task value scale (6 items) the reliability is α = .90.
The reliability of the task value scale for the current study is somewhat consistent
with the MSLQ. However, due to the means restricted range problem of responses
(7.28, 7.10, and 6.98, where the potential range was: 2–8), it was expected that the
self-efficacy coefficient would have a lower reliability (actual was .44, .66, and .54).
From the analysis of the data collected in the study, a conclusion was made
that human performance professionals who self-report higher self-efficacy and higher
task value will choose the task suggested within each of three different human
performance work-related problems. This conclusion was based on the statistical
analyses that rejected the hypothesis that there is no relationship between the
variables. Hence, the data analysis suggests that there is a relationship between the
variables.

54
Furthermore, the study indicated that human performance professionals more
often chose to solve knowledge / skill problems (89%) rather than either work
motivation (77%) or work environment problems (64%). This finding was in line
with initial expectations.
Conclusions
In conclusion, this study revealed that a human performance professional’s
beliefs in their self-efficacy to solve human performance problems and the value they
had for the task itself strongly correlated with their choice to solve three types of
human performance problems (knowledge / skill problems, work motivation
problems, and work environment problems). Between self-efficacy and task value,
self-efficacy showed the greater relationship when compared to task choice. As many
studies have suggested, self-efficacy is a strong factor in any motivation model (Blair,
O’Neil, & Price, 1999; Lin, 1999; Mulkey & O’Neil, 1999; Pintrich, Smith, Garcia, &
McKeachie, 1991; Wang, 1997).
This study also provides evidence that the sample of human performance
professionals more often choose to solve knowledge / skills problems rather than
work motivation or work environment problems. The self-efficacy measure of the
survey respondents' perceived ability to accomplish the tasks that were suggested in
all three scenarios was high. Generally, respondents were confident, across all
scenarios, with regard to both their ability to accomplish the tasks and their skill to
perform the tasks. On the other hand, they did not value the tasks of work motivation

55
problem solving and work environment problem solving as much as solving
knowledge / skill problems.
Additional Findings
From the results of the survey process, the following highlights additional
findings. Several people gave feedback that it was difficult to just answer the question
and not be able to comment about the situation in the scenario. One person chose not
to proceed taking the survey because they strongly disagreed with the way it was
designed. They said: [xxx DJ: add mention that approximately 10 people had a
concern and here is what one person said]
I attempted to complete this survey. I find that I cannot, because I
don't agree with the way that the problems are categorized. The
first problem is especially troubling. It first talks about
documentation being incorrect and then goes on to describe the
problem as being one of needing training for people to use
software. Shouldn't we be looking at the total system for input to
what the problem is and then look to solve the causes? The
"complete performance analysis" described in each of these
examples doesn't give me answers to the questions I have. Sorry I
can't help you. I find closed-end questionnaires difficult to
complete. I don't know what value they can have to the recipient
when the responder isn't allowed to give real input.

There were a handful of Human Performance consultants who felt that the
scenario content was too ambiguous and did not provide sufficient systematic detail.
The author is in agreement with the observation. A Human Performance consultant
would normally incorporate a combination of interventions, possibly including
knowledge and skill, work motivation, and work environment interventions. To offer

56
a work situation and ask if they would resolve this using only one of the intervention
types seemed to frustrate some, as one recipient states below.
I answered the questions, but I wanted to argue with them. For
example, I wanted to let the nurses spend more time with the
patients. But decided to find a way to make the hospital more
profitable and get the dumb forms filled out. Maybe we'll have the
Candy Stripers do that. Oh, now that's an environmental solution.
And if fixing the documentation on the first scenario would do the
trick, then it's not a knowledge and skill problem. So I feel slightly
dissonant, but I think my sense of self-efficacy is intact.

Delivering a web-based survey offered many challenges for some participants.
About 10% of the invitations to participate emails came back with "Undelivered Mail
Returned to Sender" message. Some of the reasons for returned email were the
following: host not found, addresses had permanent fatal errors, recipient's mailbox
unavailable, access denied, the recipient name is not recognized, and user unknown.
About 25 individuals had browser incompatibility due to either using an older
browser version that was not compatible with the survey software or possibly having
equipment that did not accommodate this state-of -the-art technology. These
individuals were accommodating by filling out an attached document and emailing it
back. [xxx DJ: rework sentence?]
Some of the domain names of the email addresses added information about
the participants. Twenty-three percent of the surveys came from academic domains
(i.e., their email address had the suffix .edu). Some of the universities members that
participated in the survey include (top 10 frequency count): Ohio State, Florida State,

57
Pennsylvania State, University of Michigan, Indiana University, Boise State,
Michigan State, University of Georgia, Carnegie Mellon, Stanford.
Seven percent came from email domains outside of the United States. Survey
participants’ emails had the following country extensions: Australia (.au), Azerbaijan
(.az), Canada (.ca), Cayman Islands (.ky), Chile (.cl), Colombia (.co), Cyprus (.cy),
Cominican Republic (.do), Germany (.de), Greece (.gr), Hong Kong (.hk), Iceland
(.is), Japan (.jp), Mexico (.mx), Singapore (.sg), Turkey (.tr), Uganda (.ug), and
United Kingdom (.uk) (see Appendix D).
In addition to the technical difficulty that the media offered, there appeared to
be an emotional side that this media-type brought forth in people. One extreme
comment was: "don't send me this stuff.......who are you anyway......what made you
think it was ok to send this stuff?" Five people asked to be taken off of the list of
email names. Four asked, "where did you get my email?" Once they were told what
public source their email came from, they proceeded to take the survey. This
probably follows the thinking of four other people who questioned the legitimacy of
the survey. One commented that the web address appeared phony, that they have
received sham emails before. Many of the concerned participants emailed ISPI to ask
if this was legitimate. The four who questioned the legitimacy, traced back to the
sponsoring organization that they were aligned to, either ISPI or USC, to obtain
verification. To alleviate this in the future, it might be beneficial to have the
sponsoring organization send out the email requests to their members.

58
Lastly, a lesson learned out of this type of survey delivery media involves
rules-of-etiquette, or in the vernacular of the Internet, "netiquette". A respondent
indicated disturbance that they were included with several other email addresses such
that anyone can see. Their feedback was that it would have been polite to send the
email request to take the survey with all of the recipient's names as a blind-carbon-
copy (bcc). This way each person who received their email request would only see
their name and no other names. One other person simply suggested using blind-
carbon-copy, but was not upset with the current situation.
Implications
There are several implications that can be derived from the findings of this
study. These implications are directed at human performance academic curriculum,
human performance consulting work in the industrial setting, and future work
motivation studies.
The results of this study showed that human performance professionals who
did not choose to solve the work problems articulated in the scenarios possessed less
self-efficacy and valued the tasks less than those who chose to solve the problems.
Interventions [xxx HARRY: what types?] need to be put in place for those individuals
who are not self-efficacious [xxx DJ: such as training, or current examples] or do not
see the value [xxx DJ: presentation of case studies] in solving work motivation or
work environment problems. These intervention skills training could be added to the

59
curriculum of masters or doctoral work, professional society seminars, and / or
industrial training classes for their human performance consultants.
In consideration of the study’s data regarding the number of individuals who
did not choose to solve work motivation and work environment problems, this finding
indicates that more emphasis (and / or time) should be placed on learning how and
why these types of human performance problems could be solved.
Further research should be done in the context of the aforementioned
implications and recommendations. The research should be targeted on developing
technologies that could be instrumental in both learning about the structure of how
workers are motivated to accomplish work and how human performance consultants
can reliably accomplish the work of performance consulting (Clark & Estes, 1999).
Cost benefit research needs to be conducted to validate these interventions. This will
ensure that industrial management would consider only those interventions that cost
less to implement than the problem cost.
According to findings in the pre-adult educational research Pintrich and
Schunk (1996) argue that raising students’ self-efficacy is expected to facilitate their
academic achievement. Many studies suggest such human performance strategies as
goal setting, modeling, personalized reward, and verbal persuasion (Schunk &
Zimmerman, 1994; Locke & Latham, 1994; Ford, 1992). The reliability, validity, and
efficiency of these strategies could be the focus of additional research in the area of
work motivation.

60
This study shows that there is a greater degree of difference in the perceived
task value than self-efficacy that was exhibited in the context of choosing to solve
human performance problems. Eccles and Wigfield’s (1995) research showed that
the perceived task value (interest, importance, and utility) of a task significantly
influenced the motivation of individuals. Therefore, interventions related to raising
the level of perceived interest, importance, and utility should be investigated within
the context of the industrial work setting.
Although the sample of survey respondents originated from many locations
outside of the United States, the vast majority of respondents (based on e-mail
addresses) were from the United States. Thus, this study should be replicated for an
audience outside of the Unites States with a sponsor such as the International
Federation of Training and Development Organization (IFTDO) providing a sample
of their membership list. This recommended study could provide generalizability
evidence that the positive relationship between the motivational factors of self-
efficacy, task value, and task choice is a global phenomenon for the human
performance professional community.

61
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Appendix A: Invitation to Participate in the Study E-Mail

Subject: Quick Web Survey Questionnaire
Date: February 19, 2000

***** YOUR INPUT IS NEEDED *****

The University of Southern California, in cooperation with the International Society for Performance
Improvement (Dr. Roger Addison, Director of Human Performance Technology) is conducting a web-
based survey (hosted by The Saratoga Group).

For your 10-MINUTE investment, you will receive the results of this survey. The survey results will be
used to recommend academic and professional program changes in human performance technology and
consulting.

HERE IS THE WEB ADDRESS: http://38.245.202.100/partner/

The survey is confidential – you will be asked to provide your name and e-mail as a quick computer
registration security check.

=>=>=>=> PLEASE INVITE YOUR COLLEGUES TO TAKE THIS SURVEY <=<=<=<=

****** Complete the survey by MARCH 3, 2000 ******

67
Appendix B: Motivated Strategies for Learning Questionnaire (MSLQ)
The following are the original questionnaire items used by the MSLQ (Pintrich, Smith,
Garcia, & McKeachie, 1991, pp. 11 & 13) that are matched with the current study’s
questions.

Task Value
MSLQ Value Component
Items
Current Study Items
23. I think the course
material in this class
is useful for me to
learn.
(Utility)
2. I think solving knowledge and skills performance problems
is valuable.
I think solving work motivation performance problems is
valuable.
I think solving work environment performance problems is
valuable.
17. I am very interested
in the content area
of this course.
(Interest)
3. I am very interested in solving training problems.
I am very interested in solving work motivation problems.
I am very interested in solving work environment problems.

Self-Efficacy
MSLQ Expectancy
Component Items*
Current Study Items
5. I believe I will
receive an excellent
grade in this class.
(Self-Efficacy)
4. I believe I would be very successful if I implemented a training
solution for the above scenario.
I believe I would be very successful if I implemented a work
motivation solution for the above scenario.
I believe I would be very successful if I implemented a work
environment solution for the above scenario.
15. I’m confident I can
do an excellent job
on the assignments
and tests in this
course.
(Self-Efficacy)
5. I am confident that I could do an excellent job in solving the
knowledge/skill problem described in the above scenario.
I am confident that I could do an excellent job in solving the
work motivation problem described in the above scenario.
I am confident that I could do an excellent job in solving the
work environment problem described in the above scenario.
* Self-Efficacy for Learning and Performance Items

68
APPENDIX C: Questionnaire Survey

69

Please provide the following information about yourself. This information will remain confidential. It will be
used to describe the group of individuals who have completed this survey.
Professional Society Membership?(select as many as apply):
ASTD ISPI SIOP Other
Required Field *
Press the Continue button to begin the survey:
First Name:*
Last Name:*
Years of Total Work Experience:*
Email:*
Years practicing human performance improvement:*

70

You are a human performance and training professional with plenty of work to do. You have just become
aware of some additional work. Read the three human performance problem scenarios on the following pages
and decide whether or not you would want to work on these problems.

71

71

72

72

73

74
Appendix D

Education International
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Singapore (.sg)
Turkey (.tr)
Uganda (.ug)
United Kingdom (.uk)
Education
Commercial
International
Network
Military
Other
Nonprofit

Abstract (if available)

Abstract The improvement of human performance is the goal of individuals and organizations that contribute to that endeavor in the workplace. Decisions are made to suggest training as an intervention when there is no evidence supporting a lack of knowledge and skills in the target population. Training being offered as the only solution is often due to a lack of confidence in or value for alternative performance improvement interventions. Thus, this study involves those people who decide what interventions to apply to improve human performance.

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Asset Metadata

Creator Price, Donna J. (author)

Core Title Factors associated with an HPT professional's choice to solve work motivation problems

School Rossier School of Education

Degree Doctor of Education

Degree Program Human Performance Technology

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag OAI-PMH Harvest

Language English

Advisor Hocevar, Dennis (committee chair), Blair, Daniel V. (committee member), O'Neil, Harold F. (committee member)

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m386

Unique identifier UC1329566

Identifier etd-Price-200004 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-402833 (legacy record id),usctheses-m386 (legacy record id)

Legacy Identifier etd-Price-200004.pdf

Dmrecord 402833

Document Type Dissertation

Rights Price, Donna J.

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions (213) 743-1672; https://www.usc.edu/isd/libraries/locations/grand/

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