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1
Scientists, Engineers, or Both? Motives and Preferences of Technical Professionals
in Today’s Scientific R&D Organizations
By Isabel Bignon
B.S. in Industrial Engineering, September 2007, Universidad de Santiago de Chile, Chile
A Dissertation submitted to
The Faculty of
The School of Engineering and Applied Science
of the George Washington University
in partial fulfillment of the requirements
for the degree of Doctor of Philosophy.
January 31, 2016
Dissertation directed by
Zoe Szajnfarber
Assistant Professor of Engineering Management and Systems Engineering
ii
The School of Engineering and Applied Science of The George Washington University
certifies that Isabel Bignon has passed the Final Examination for the degree of Doctor of
Philosophy as of October 20, 2015. This is the final and approved form of the
dissertation.
Scientists, Engineers, or Both? Motives and Preferences of Technical Professionals
in Today’s Scientific R&D Organizations
Isabel Bignon
Dissertation Research Committee:
Zoe Szajnfarber, Assistant Professor of Engineering Management and Systems
Engineering, Dissertation Director
Julie J.C.H. Ryan, Associate Professor of Engineering Management and Systems
Engineering, Committee Member
Ekundayo Shittu, Assistant Professor of Engineering Management and Systems
Engineering, Committee Member
iv
Dedication
I dedicate my dissertation to my parents Veronica and Marcel, my husband Pablo, my
brother Pedro, and my son Victor.
v
Acknowledgements
First and foremost, I would like to express my immense gratitude to my advisor Prof. Zoe
Szajnfarber for the support and guidance throughout this process. Thank you for being
understanding and for helping me getting through the wandering stages in my research.
Your encouragement and guidance always made me feel challenged, capable and
empowered. You are an exemplary mentor and an inspiration to me.
I would like to acknowledge my dissertation committee of Prof. Mazzuchi, Prof. Ryan,
Prof. Shittu and Deborah Amato, for their insightful comments and for their support
throughout these years.
I thank the Szajnlab and other EMSE friends for challenging and enriching my work.
Thank you for always being there to help and to encourage each other. I am so lucky to
have been able to work and have fun with not only incredibly smart people, but also great
friends.
This research would not have been possible without the respondents and informants from
NASA who took part in this study. I would also like to acknowledge the GWU
Department of Systems Engineering and Engineering Management and Fulbright-Chile
for their financial support in my doctoral studies.
Last but not the least, I would like to thank my family for always being there for me.
Your love, support, and encouragement kept me going throughout this journey. Thank
you to my mom, Veronica, and my dad, Marcel, for teaching me about hard work,
persistence, and independence. I deeply appreciate all those times when you went through
frustrations and joys with me. Your unconditional support and love has been fundamental
vi
to my accomplishments. Thanks to my brother, Pedro, for being an example to me. I have
always been inspired by your intellectual capacity and your beautiful soul. Thank you to
my son, Victor, for keeping me sane and connected to what is important in life: love and
family. Finally, I would like to thank my husband, Pablo, for being so patient and
incredibly supportive. I could not have done this without you by my side. Thank you with
all my heart and soul. I am so lucky to have you in my life.
A portion of this dissertation is part of the paper below; however, the contents of this
document are original for the purposes of this dissertation.
Bignon, I., & Szajnfarber, Z. (2015). Technical Professionals’ Identities in the R&D
Context: Beyond the Scientist versus Engineer Dichotomy. Engineering Management,
IEEE Transactions on, 62(4), 517-528.
vii
Abstract of Dissertation
Scientists, Engineers, or Both? Motives and Preferences of Technical Professionals
in Today’s Scientific R&D Organizations
Scientists and engineers (S&Es) are fundamental pillars of technical organizations.
Managing the intellectual human capital is challenging and critical to organizational
success. The literature that deals with the management of S&Es remains grounded in
theory developed by studies made in the 1950s and 1960s. Although the context and
characteristics of this workforce have changed over the years, deeply embedded
assumptions and broad generalizations about S&Es remain the same in modern literature.
There is a need to revisit and update the underlying assumptions about technical
professionals through deep empirical work in order to keep management of technical
organizations connected to the reality of today’s workforce. From a practical perspective,
managers need to understand their employees’ motivations to be able to properly
incentivize them. This research aims to answer the following questions: What motivates
scientists and engineers today? How do scientists and engineers respond to different
incentives? How can this knowledge be used to improve incentives for scientists and
engineers? We take two approaches to answer these questions. First, we quantitatively
test old assumptions about motivations of S&Es in a large and current data set. We found
that there is no strong support for clear-cut distinctions between scientists and engineers;
viii
this is not to say that there are not meaningful differences in categories of employees just
that those differences do not fit cleanly along scientist and engineer lines. Also, we found
that commonly used measures on motivation and job satisfaction have limited usefulness
to managers trying to create effective incentives for their technical personnel. To improve
the situation, we use an inductive approach to develop better measures, qualitatively
exploring what motivates S&Es and how they react to different incentives. We found that
S&Es have a variety of motivations for work that can be grouped in three dimensions:
social, temporal, and technological. Individuals’ preferences within each dimension
influence the way they react to incentives. For example, a scientist or engineer with a
social orientation will favorably react to incentives that involve interaction with others
such as taking on management roles. Our results call for more attention to the variety of
orientations within the workforce as a way to improve the management of scientists and
engineers in today’s technical organizations.
ix
Table of Contents
Dedication iv
Acknowledgements v
Abstract of Dissertation vii
Table of Contents ix
List of Figures xii
List of Tables xiii
Chapter 1: Introduction 1
1.1 Statement of the Problem 1
1.2 Research Questions and Method 3
1.3 Overview of the Dissertation 5
Chapter 2: Literature Review 6
2.1 Technical Professionals in Industrial Research Laboratories 6
2.2 Connecting Management Strategies to Dichotomies 12
2.3 First Steps Towards More Nuanced Scales 14
2.4 Motivation and Job Satisfaction as the Basis for Identity Classification 16
2.5 Summary of the Literature 17
Chapter 3: Research Approach 19
3.1 Phase 1: Testing Assumptions about Scientists and Engineers in a Large Dataset 20
3.1.1 Data and Sample 20
3.1.2 Deductive Approach 23
3.1.3 Logistic Regression 25
3.2 Phase 2: An Inductive Approach to Exploring Motivations of Scientists and Engineers 26
x
3.2.1 Grounded theory method 27
3.2.2 Research Context 29
3.2.3 Sample 30
3.2.4 Data Analysis 31
Chapter 4: Testing Old Assumptions about Scientists and Engineer’s in a Large
Dataset 34
4.1 Data description 34
4.2 Inferential statistics 39
4.3 Testing old hypotheses 41
4.4 Selecting key variables for detailed follow-up 43
4.5 Comparing preference for incentives using multiple criteria 45
4.6 Discussion 48
Chapter 5: An Inductive Approach to Exploring Motivations of Scientists and
Engineers 52
5.1 Dimensions of motivation 54
5.1.1 Social orientation 54
5.1.2 Temporality of reward 55
5.1.3 Involvement with technology 56
5.2 Work identities 56
5.3 Discussion 60
5.3.1 Identities in the Traditional Dichotomy 61
5.3.2 Understanding and Using Incentives based on Identities 63
Chapter 6: Conclusions 72
6.1 Contributions 73
6.2 Limitations 75
xi
6.3 Future research 76
References 79
Appendix A - Interview Guide 88
Appendix B - Logistic regression complete results 90
Appendix C - Ranking of odds-ratios for combination of criteria 93
Appendix D - Predicted probabilities by work activity 94
Appendix E - Predicted probabilities under different scenarios 98
Appendix F - Quotations examples by code (summary) 104
xii
List of Figures
Figure 3-1. Overview of the research approach 20
Figure 3-2. Research approach in Phase 1 24
Figure 3-3. Grounded theory method 28
Figure 4-1. Adjusted predictions of job satisfaction for engineers 47
Figure 4-2. Adjusted predictions of job satisfaction for biologists 48
Figure 5-1: Model of Motivations and Incentives 53
Figure 5-2: Miscategorization of identities 62
xiii
List of Tables
Table 2-1: Dichotomies of professionals in the literature 8
Table 2-2: Some taxonomy of scientists in the literature 15
Table 3-1. Variables in Phase 1 23
Table 4-1. Descriptive statistics: frequencies in descending order 35
Table 4-2. Descriptive statistics: job satisfaction 36
Table 4-3. Tetrachoric correlations 36
Table 4-4. Contingency table with percentages, part 1 37
Table 4-5. Contingency table with percentages, part 2 38
Table 4-6. Rank of odds-ratios for logistic regression with all data with alpha 0.5 40
Table 4-7. Logistic regression result in odds-ratios for scientists 41
Table 4-8. Logistic regression results in odds-ratios for engineers. 42
Table 4-9. Odds-ratio ranking for each criterion 44
Table 4-10. Contingency table for field of study by job code 44
Table 4-11. Results of logistic regression for combined criteria 45
Table 5-1. Dominant action codes by identity (Bignon & Szajnfarber, 2015) 57
1
1.1 Statement of the Problem
Intellectual human capital is an essential part of technology-intensive organizations
(Hess & Rothaermel, 2012; Rothaermel & Hess, 2007; Subramanian, 2012).
Organizational success is integrally tied to the way technical employees are managed
(Bailyn, 1991; Jain et al., 2010). But managing this kind of workforce is not trivial. As
Glaser (1965) puts it, “research performance, unlike many other kinds of work, cannot be
enforced. Rather, it must come as a product of the enthusiasm that an individual feels
toward his work.” Thus, one way in which managers can influence performance in this
context is by incentivizing and motivating their technical staff (Glaser, 1965; Hebda et
al., 2007; Van Knippenberg, 2000). Although there is extensive literature on motivation
(e.g. (Herzberg, 1966; Latham & Locke, 1979; Maslow et al., 1970; McClelland, 1961)),
work motivation of technical professionals in the research environment remains
understudied (Ryan, 2014). There is also conflicting literature on the motivation of
scientists and engineers. While some authors differentiate between their motivations
(Amabile, 1997; Badawy, 1971; Hebda et al., 2012; Keller, 1997; Kerr et al., 1977;
Petroski, 2010), others put them all in the same box (French, 1966; Sauermann & Cohen,
2010). Most of the literature in this area is based on seminal studies conducted in the
1950s and 1960s where characteristics of the scientist and engineer were outlined (T.
Allen & Katz, 1986; Gouldner, 1957; Gouldner, 1958; Shepherd, 1961). Research
thereafter mainly adopts these definitions without testing them. However, between then
2
and now, contextual factors have changed drastically and the characteristics of the
technical professional workforce have changed as well.
Scientists and engineers in the R&D organization are an important population to
understand. They play a central role in technological innovation and economic growth
(U.S. Department of Commerce, Economics & Statistics Administration, 2014), and they
constitute a growing portion of the workforce population both in quantity and importance
(U.S. Department of Commerce, Economics & Statistics Administration, 2014). In the
U.S., the average annual growth rate of people in science and engineering occupations is
twice as fast as the growth rate for the total workforce (3.3% compared to 1.5% growth
between 1960 and 2011) (National Science Foundation, National Center for Science and
Engineering Statistics, 2014). Furthermore, it is expected that the demand for
professionals in STEM (science, technology, engineering, and mathematics) fields will
increase (U.S. Department of Commerce, Economics & Statistics Administration, 2014).
To keep up with this demand and maintain its preeminent competitiveness, the U.S.
government is implementing strong initiatives to promote and improve STEM education
(U.S. National Science and Technology Council, 2013). As the number of scientists and
engineers that are attracted to these disciplines increases, the variety of their motivations
will increase too. Studies in the 1960s acknowledged the changes in these professionals’
characteristics and motivations with respect to these professionals in the past. Likewise as
the trend was forecasted to keep increasing, they predicted more changes for the future
(Danielson, 1960).
In spite of the sustained increase in numbers and relevance to technical organizations
and the economy in general, efforts to understand scientists’ and engineers’ motivations
3
have not kept up with their growth. Moreover, motivational assumptions about technical
professionals have rarely been challenged.
To the extent that technical professionals have been studied, they have been viewed
through the lenses of motivation (Locke, 1991), incentives and rewards (Owan &
Nagaoka, 2011), and career progression (Holland, 1996). At the individual level, several
seminal studies conducted in the 1950s and 1960s (Gouldner, 1957; Gouldner, 1958;
Shepherd, 1961; T. Allen & Katz, 1986) set the basis of what is known about technical
professionals, mostly focusing on characterizing scientists and engineers. However, in
today’s context, clear-cut distinctions between scientists and engineers are less common.
Something that has not changed over the past several decades, however, is the importance
of identifying, recruiting, and retaining good, highly motivated employees. Managers
must be able to both find the tools to preserve motivation of their employees and to
maintain adequate workforce conditions to improve organizational performance. This
cannot be done without a deep and up-to-date understanding of today’s technical
professionals’ motivators. As such, there is a need to revisit the basic assumptions of the
behavioral models of technical employees through deep empirical research in order to
keep the models connected to the reality of work today.
1.2 Research Questions and Method
The purpose of this research is to provide a more current and nuanced understanding
of technical professionals’ motivations to improve workforce models in the literature and
management practice of this type of intellectual human capital. More specifically, this
research intends to answer the following questions: first, what motivates scientists and
engineers today? Second, how do they respond to different incentives? And third, how
4
can managers use this knowledge to improve incentives for scientists and engineers? To
answer these questions we take two approaches: (1) we use a deductive approach to test
whether common assumptions about scientists’ and engineers’ motivations, which were
based on the workforce of 1950s and 1960s but continue to pervade today’s literature,
still apply. We do this with a statistical analysis of a large and current dataset. Our results
indicate that both the theory and characteristics of the data are not sufficient to answer
our research questions, thus (2) we use an inductive approach to explore the underlying
motivations of scientists and engineers today. This last approach lets us build theory that
lets us answer the second and third research questions. Finally, we discuss how our
results can inform management practices, and in particular, how can we use what we
learned to create more effective and efficient incentives for scientists and engineers in
technical organizations.
Consistent with the two approaches mentioned above, this research is presented in
two phases: first, we take an available dataset with information about contemporary
scientists and engineers in the United States and perform statistical analysis that lets us
test some of the most embedded assumptions in the literature about the motivations of
scientists and engineers. Then, we develop our exploratory, qualitative research using a
specific scientific-R&D organization that employs both scientists and engineers.
Our results indicate that there are a variety of work motives among technical
professionals that do not map to the classic scientist versus engineer dichotomy.
Moreover, the way we define scientists versus engineers changes our understanding about
them. Our theory points to a more nuanced management approach based on underlying
dimensions of motivation.
5
1.3 Overview of the Dissertation
The remainder of this dissertation is structured in four main parts. Chapter 2 presents
a review of the relevant literature that frames our research problem. Chapter 3 explains in
detail the research approach that allows us to answer our research questions. Specifically
we use two approaches: we take a deductive approach to test old assumptions about
motivation and then an inductive approach to explore and build theory on the same
concept. Chapters 4 and 5 describe the analysis and results from the quantitative and
qualitative studies, respectively. We wrap things up in Chapter 6 where we summarize
our findings and discuss the implications of our research for managers and scholars.
6
This section describes in detail the existing scholarly work that frames our research
problem. We performed a review of the R&D management literature centered on the
characteristics and motivations of technical professionals specifically those of scientists
and engineers. Furthermore, we reviewed literature in areas such as sociology of science,
motivation, psychology, personality, and human resources.
Chapter 2 is structured in four parts. First, we review the historical understanding of
technical professional orientations. Second, we look at how those definitions have
influenced management. Third, we show some of the recent efforts towards a more
nuanced understanding of technical professionals. And lastly, we argue that the study of
motivation is a valuable path to update the foundations of what is known about scientists
and engineers.
2.1 Technical Professionals in Industrial Research Laboratories
Industrialization and higher specialization of professionals greatly accelerated the
growth of technical organizations, introducing new organizational challenges. One of
those challenges was, and arguably still is, the management of a highly trained
workforce. Although it is widely accepted that technical professionals play a central role
in technology-intensive firms (Subramanian, 2012), what is known about their
preferences has been typically reduced to general characterizations of scientists and
engineers.
One of the first efforts towards understanding technical professionals’ orientations
was the local-cosmopolitan construct (Delbecq & Elfner, 1970; Gouldner, 1957;
7
Gouldner, 1958; Shepard, 1956), which was inspired by the work of Merton (1948) on
the influential roles of community members. This literature mainly focused on the
tensions between autonomy and organizational goals (Bailyn, 1985) at the individual
level. In this view, cosmopolitans were broadly defined as profession-oriented employees
who were interested in success in their field of expertise; locals, on the other hand, were
identified as ‘good company men’ who were interested in promotion within the
organization (Shepard, 1956). Several authors used the local-cosmopolitan dichotomy to
describe the differences between scientists and engineers (Ritti, 1968; Shepherd, 1961).
Although the local-cosmopolitan construct was popular for about two decades, it was
largely abandoned in the organizational literature after 1980 mainly due to
operationalization problems and dimensionality questionings (Berger & Grimes, 1973;
Grimes, 1980; Grimes & Berger, 1970).
Although the use of the local-cosmopolitan construct was largely discontinued, its meaning meaning survived nested in the scientist versus engineer dichotomy (T. Allen, 1984; Badawy, Badawy, 1971; Kerr et al., 1977).
Table 2-1 displays some of the conceptualizations of locals versus cosmopolitans and scientists versus engineers found in the literature. This table is organized chronologically and includes the area of focus for categorization, a brief description of each element of the dichotomy, the research method utilized, and characteristics of the sample chosen. We will reference this table again later on in this literature review.
Table 2-1 is not intended to show a comprehensive review of the literature; instead it
focuses on defined dichotomies presented by some authors.
Table 2-1: Dichotomies of professionals in the literature
8
Author Area of focus Types of
professionals Description Method Sample
Gouldner
(1957;
1958)
Reference
groups
(organizational
or professional,
internal or
external)
Locals vs.
cosmopolitans
(and
subcategories of
each).
Locals: mostly loyal to the
organization, internal
reference groups.
Cosmopolitans: mostly
loyal to the profession,
external reference groups.
Survey
and
factor
analysis
Teachers,
researchers
and
administrators
Shepherd
(1961)
Goal
orientations,
reference
groups, and
supervision
Scientists vs.
engineers
(cosmopolitan
vs. locals)
Engineers: supervision and
development,
organizationally oriented,
do not ignore managerial
activities. Locals.
Scientists: pure and applied
research, professionally
oriented, and ignore
managerial activities.
Cosmopolitans.
Survey Engineers and
scientists
Glaser
(1963)
Congruence of
institutional and
organizational
goals, and
professional
motivation
Locals,
cosmopolitan,
local-
cosmopolitans
Locals: low on
professional motivation
Cosmopolitan: high on
professional motivation
and different goals.
Local-cosmopolitans in
basic research: high or
medium professional
motivation and same goals.
In applied research, goals
are different.
Survey Research staff
Ritti
(1968)
Reference
groups
(organizational
or professional,
internal or
external)
Work goals
Locals vs.
cosmopolitans
(engineers vs.
scientists)
Locals: engineers
Cosmopolitans: scientists
Scientists: publications and
professional autonomy
Engineers: align with the
goal of business (e.g.
meeting deadlines,
marketable products)
Survey
and
factor
analysis
Engineers and
PhD scientists
Badawy
(1971)
Motivation
(importance of
money,
direction of
motivation, job
orientation)
Scientists vs.
engineers
(cosmopolitan
vs. locals)
Scientists: money is not so
important; motivated by
meaningful work and
autonomy, recognition is
very important;
professional orientation
(cosmopolitan).
Engineers: money is
important, organizational
orientation (local).
Survey
and
literature
review
Scientists
9
Grimes
(1980)
Commitment
(organizational
or professional),
career strategy
(immobility or
advancement),
and reference
group
Pure locals,
pure
cosmopolitans
and other
combinations of
dimensions
E.g. Pure cosmopolitan:
high on professional
commitment, concern for
advancement, and external
reference group
orientation. Low on
commitment to
organization and
organizational immobility.
Pure locals: the opposite.
Survey
and
factor
analysis
University
faculty
Keller
(1997)
Job
involvement
and
organizational
commitment
Scientists vs.
engineers
Job involvement is much
more of a motivator for
R&D performance for
scientists than for
engineers.
Organizational
commitment is not related
to performance or job
involvement on either
scientists or engineers.
Survey Scientists and
engineers
Depending on the nature of the study, scientists and engineers have been regarded as
either (1) a cluster of similar-minded professionals (French, 1966), (2) two groups with
different orientations (T. Allen, 1984; Keller, 1997; Kerr et al., 1977; Petroski, 2010), or
(3) as one heterogeneous group of R&D professionals, which may also include technical
managers (Badawy, 1971; Grimes & Berger, 1970; Schein et al., 1965)
In this context, and generally speaking, scientists’ and engineers’ differences can be
organized based on educational background, work activities, and preference for
incentives. In the following paragraphs we will describe scientists and engineers’
differences with respect to these aspects.
Scientists and engineers with the possible exception of engineers with PhDs (Bailyn,
1985) are different because they choose and go through different socialization processes
in their education (T. J. Allen & Katz, 1992; Danielson, 1960; Ritti, 1968). As Danielson
(1960) puts it, “[y]ears of schooling promote and perpetuate certain knowledge, skills
and attitudes that distinguish one profession from another. Hence, the formal schooling
acts as a standardizing or stabilizing influence regardless of the characteristics of the
10
students attracted and selected” (p. 30). An engineer was normally described as someone
with a Bachelor’s degree in engineering who transitioned directly into the workforce
(Ritti, 1968). Anyone with a PhD was classified as a scientist (Pelz, 1967), making the
distinction a function of an assumed research orientation (T. Allen, 1984; Andrews &
Pelz, 1966).
Different work activities attract people with diverse orientations (Bailyn, 1985).
Moreover, the group differences between scientists and engineers anticipate the work
activities that would be more satisfying and dissatisfying to them (Danielson, 1960). On
the one hand, scientists with PhDs prefer basic research whereas non-PhDs prefer applied
research or development (Andrews & Pelz, 1966). Engineers, on the other hand, aspire to
positions in management (Danielson, 1960; Raudsepp, 1963; Ritti, 1968; Shepherd,
1961) or development (Shepherd, 1961).
With respect to preferences for incentives, scientists especially those with PhDs
(Ritti, 1968) and compared to engineers (Kerr et al., 1977; Ritti, 1968; Wilensky, 1964)
highly value independence 1 (Box & Cotgrove, 1968; Pelz, 1967). Moreover, Glaser
(1963) argues that scientists who are happy with their level of independence will be
happy no matter what work activity they do. According to Raudsepp (1963), although
independence is an important factor of job satisfaction of creative scientists and
engineers, the most important aspect of the job is having intellectually challenging work
(Raudsepp, 1963). Another important factor is opportunity for advancement. Both
scientists and engineers care about advancement (Ritti, 1968), as it reinforces their
1 In this research we use independence and autonomy as equivalent concepts that refer to the ability of an
employee to define what to work on. For an in depth discussion of autonomy in the industrial R&D lab see
Bailyn (1985).
11
feeling of self-worth and professional growth (Raudsepp, 1963). However, there is an
inverse relationship between preference for advancement and preference for
independence and challenge (T. J. Allen & Katz, 1995). Engineers, in general, value
material rewards somewhat more than scientists do, “although it must be admitted that
the primary lure of industry to scientists has been higher salaries” (Raudsepp, 1963).
Salary is how our society measures success and how the organization measures status.
Salary is a symbol of achievement, status, and recognition. In spite of its known
importance, scholars and managers mistakenly interpret the “reluctance to mention
financial matters as evidence of relative nonconcern with material benefits” (Raudsepp,
1963).
Although there is overlap in scientists’ and engineers’ preference for incentives, it
could be said that independence, challenge, advancement, and salary are amongst the
most important aspects of the work that these technical professionals care about. Not only
are these motives important by themselves, but their combination affects S&E’s
motivation. As Raudsepp (1963) puts it, “the technical person is driven or pushed by a
combination of needs rather than by a single motive, and is, therefore, attracted or
repelled by a combination of interdependent and cross-related factors” (p. 163). In this
review we have identified the most important motives found in the literature.
Summarizing, the “classic scientist” would be a person who has attained the highest
educational degree in a science field and whose work includes doing basic or applied
research. The “classic engineer” would be someone trained in engineering who works in
development or management. Both the classic scientist and classic engineer care about
independence, challenge, advancement, and salary, however, they ponder these aspects of
12
the work differently. While independence is the top priority to the classic scientists, it is
the last priority to the classic engineers. For the rest of the motivators there is less
concurrence of results about their relative importance.
What used to differentiate scientists and engineers is now more equivocal. For
example, engineering doctorates are common in many sectors and they work side by side
with scientists in different technical jobs. Moreover, the number and diversity of people
going into STEM fields has been steadily increasing over time. With this, it is very likely
that the breadth of orientations within this workforce has also increased. Thus, the
content of what used to be considered engineering versus science has become more
complicated in practice today. Yet current literature keeps using and assuming old
characterizations of scientists’ and engineers’ motivations. Therefore, there is a gap in
today’s R&D management literature with regards to keeping old models connected to the
present. In this research we aim to test whether those old models of scientists’ and
engineer’s motivations represent today’s technical workforce. If we find that they do not
represent today’s workforce motivations, we aim to understand what those motivations
are. Identifying the factors that drive those motivational changes in the technical
workforce is out of the scope of this research and should be investigated in future
research.
2.2 Connecting Management Strategies to Dichotomies
Managing personnel with different orientations like the ones described above poses
important managerial dilemmas (Shepard, 1956) such as varying incentive systems,
supervision styles, job assignments, and career progressions (Delbecq & Elfner, 1970;
Glaser, 1963). Managerial promotion, for example, used to be the most important reward
13
for good scientific work. But rewarding scientific achievement with promotion to a path
that requires a different set of skills does not make much sense. As Shepard (1958) puts it
“when a good scientist is made a manager, a good scientist is lost,” and certainly a good
manager is not guaranteed. In such environments, career aspirations of technical
employees who were not interested in management did not match existing rewards. This
issue forced organizations to change the definitions of success behind alternative career
paths (Goldner & Ritti, 1967). Hence, the ‘dual ladder’ system emerged from the
management practice as a response to the need for more suitable and rewarding career
opportunities to keep technical professionals in their technical area. Specifically, the
technical ladder was intended to provide increased status and better salary as the
management ladder does but it was also intended to offer more autonomy for individual
research without the burden of administrative duties (T. Allen & Katz, 1986; Goldner &
Ritti, 1967).
The local-cosmopolitan literature was also intertwined with the dual ladder literature:
locals would prefer the managerial ladder while cosmopolitans prefer the technical
ladder. As such, according to Ritti (1968) scientists (cosmopolitans) expect to build a
reputation outside the company while engineers (locals) desire internal career
development.
The dual ladder has not been exempt from criticism (T. Allen & Katz, 1986). For
example, Goldberg et. al. (1965) argue that preference for advancement is not a matter of
organizational versus professional rewards, but is instead about level of personal
gratification, independent of the source. One of the problems with the dual ladder is the
assumption that scientists, or technical professionals in general (depending on the
14
author), have no interest in the managerial ladder, which is not always true (Goldner &
Ritti, 1967). Additionally, career ladders reflect the definitions of success within
organizations (Goldner & Ritti, 1967) and when there are other orientations or
combinations of orientations that are not well understood, professionals do not find
available career paths motivating. Furthermore, paths in dual ladder systems have been
perceived in practice as not equivalent, which also decreases their attractiveness.
Organizations must address the complex endeavor of managing a diverse set of career
paths. The first step in this pursuit is to better understand the motivations of their
scientists and engineers.
2.3 First Steps Towards More Nuanced Scales
Despite the general lack of understanding of the variety of technical professionals’
orientations, in recent studies some authors have started to question the assumptions of
lower-level homogeneity (Rothaermel & Hess, 2007). For instance, Badawy (1971)
acknowledges the possibilities of several degrees of orientation between engineers and
scientists. Other authors have added nuance to the classical taxonomies, for example: star
versus non-star scientists (Rothaermel & Hess, 2007; Zucker & Darby, 1997), academic
versus industrial scientists (Dietz & Bozeman, 2005; Sauermann & Stephan, 2010), and
bridging scientists versus pure scientists versus pure inventors (Subramanian, 2012). To
date, nuance among engineer types is scarce if not non-existent. Scientists have received
more attention in the literature than engineers2, but the focus has been on topics such as
becoming a scientist and traits of scientists as a general category. Table 2-2 presents more
details about some of the taxonomies of scientists found in the literature. This table is not
2 For a good review on the psychology of science read Feist (1998)
15
intended to show a comprehensive review of the literature; instead it focuses on clearly
defined classifications of scientists presented by some authors.
Table 2-2: Some taxonomy of scientists in the literature
More broadly, some authors have proposed alternative taxonomies for knowledge
workers (where scientists and engineers are a subgroup). Davenport (1999), for example,
claims that the best criterion for segmenting knowledge workers is their job roles within
the organization, while Holsapple and Jones (2005; 2004) and Geisler (2007) classify
them by knowledge activity. These are valuable ways to understand the workforce at the
aggregated level, but they do not answer the question of what motivates the particular
subgroup of professionals that we are interested in understanding: scientists and
engineers.
Author Dimension Scientist
types Description Method Sample
Sauermann &
Stephan
(2010)
Economic
sector
(industry or
university)
Academic vs.
industrial
Found differences in
preference for salary and
desired organizational
attributes. Scientists self-
select into different
sectors.
Regression
analysis Scientists
Rothaermel &
Hess(2007) Productivity
Star vs. non-
star scientists
Defined star scientists as
more productive and
influential (by orders of
magnitude) than average
(non-star) scientists in the
same field.
Independent
variable in
statistical
analysis
Scientists
with
graduate
degrees
Bozeman &
Corley(2004)
Collaboration
strategy
Taskmaster,
Nationalist,
Mentors,
Followers,
Buddy,
Tacticians.
Found different types of
scientists based on their
preferences when choosing
collaborators.
Factor
analysis
Scientists
and
engineers
Subramanian;
Subramanian,
Lim, & Soh
(2012; 2013)
Research
outcome
(publication
or patent)
Pure
scientists,
bridging
scientists
(Edison and
Pasteur type),
and pure
inventors
Defined three types of
scientists depending on
their research outcomes:
only publications,
publications and patents,
and only patents,
respectively.
Independent
variable in
statistical
analysis
Scientists
16
A more universal kind of taxonomy is personality-based. Scholars in the
organizational psychology literature have studied personality traits as predictors of other
variables such as job performance (Barrick & Mount, 1991) and vocational interests
(Darley & Hagenah, 1955; Holland, 1997). Since studies in interests are often studies in
motivation (Berdie, 1944), and interests are an expression of personality (Holland, 1997),
it can be said that motivation and personality are related concepts. Although there is a
meaningful overlap between them, vocational interests are distinct from personality
(Larson et al., 2002). In this research we focus only on motivation for work as it directly
informs the design of incentives.
2.4 Motivation and Job Satisfaction as the Basis for Identity Classification
The motivation literature is very extensive. There are several influential theories that
inform motivation research in general (Rainey, 2000). For example, Maslow’s (1970)
well-known theory proposes that human motivation follows a hierarchy of needs, from
physiological to self-actualization needs. Another important theory is Herzberg’s (1966)
two-factor theory where ‘motivators’ (internal drivers) and ‘hygiene factors’ (external
triggers) explain motivation and demotivation in work settings. The particular literature
on work motivation however, has focused more on situational approaches while
neglecting individual differences (Furnham et al., 2009; Staw et al., 1986). Although both
of these perspectives add knowledge to the concept of work motivation, an individual-
centered approach will give us better insights into why and how technical professionals
are motivated at and by their work. More specifically, technical professionals’ motives
have been studied in relation to incentives (Roach & Sauermann, 2010; Sauermann &
Cohen, 2010; Stern, 2004) and innovative behavior (Scott & Bruce, 1994; Woodman &
17
Yuan, 2010). Similar to the literature on psychology of science, the study of technical
professionals’ motives often analyzes the characteristics of broad populations such as
scientists, engineers, or technical professionals. One of the dangers of accepting big
aggregated assumptions is their suitability to heterogeneous populations such as high-
tech, R&D organizations. Therefore, it is important for managers to understand the
variety in employees’ work motivation (Glaser, 1965) in order to provide them with
proper incentives. There is general agreement in the literature about the benefits of
having motivated and satisfied employees (Furnham et al., 2009). In this research we
focus on using empirical data on both work motivation and job satisfaction to understand
the orientations of today’s scientists and engineers. Although these concepts are not the
same, they are related. As Furham et. al (2009) put it, “it is arguable that the extent to
which an individual is satisfied at work is dictated by the presence of factors and
circumstances that motivates him or her.” Porter & Lawler (1968) argue that the role of
job satisfaction is not to be a stimulus for performance but rather an indication of how
well the organization is rewarding its employees in relation to their performance. Having
satisfied and motivated employees is key to improved utilization (Raudsepp, 1963) of the
intellectual human capital.
2.5 Summary of the Literature
As we start thinking about workforce in terms of a mix of individuals with different
aspirations and start acknowledging the advantages of diverse professional orientations,
the study of technical professionals in organizations becomes more complex but
incredibly valuable. The literature has provided us with knowledge about engineers and
scientists and some variations within those boundaries such as star scientists (Oettl,
18
2012), innovators, and inventors (Owan & Nagaoka, 2011; Subramanian, 2012). These
concepts have been frequently defined in terms of dichotomized taxonomies to help break
down complex problems. Although useful in numerous cases, this level of simplification
needs to be used with caution when applied to contexts that require higher resolutions of
information such as the R&D workforce. Hence, the need for a richer understanding of
the technical professional’s motivation is imperative so managers can stop relying on
oversimplified assumptions to design incentives (Badawy, 1971). Without a deep and
current understanding of the diversity of preferences in R&D organizations, incentives
and career paths will not be adequate and could result in undesirable behaviors.
Moreover, studies that do not carefully define their categorizations of people could be
misrepresenting their motivations. In this research, we aim to refresh and add important
nuances to technical professionals’ work motivations and identities.
19
As we have shown in the previous section, the literature on technical professionals
relies heavily on theories built decades ago that have not been updated. In this research
we use two different but complimentary approaches to answer our research questions.
First, we take a deductive approach to statistically test common assumption about
scientists and engineers in a large and current dataset (Phase 1). Then, we inductively
explore what motivates technical professionals today (Phase 2) using the grounded theory
method. In this chapter, we explain in detail how our research approaches allow us to
answer our research questions.
Figure 3-1 summarizes the research approach in which we frame and tackle our
research goals. This figure represents the characteristics and connections of the elements
that let us answer our research questions. Starting from the top and looking at the figure
in a counterclockwise direction, we begin with a literature review that directly feeds into
the hypotheses that are tested in Phase 1. For this phase, we adopt a deductive approach:
we formulate our hypotheses based on existing theory and analyze observations (data)
with the aim of accepting or rejecting our hypotheses. Phase 1 lets us answer our three
research questions, contribute to the literature and suggest practical contributions.
However, as we will show later in this dissertation, we rejected Phase 1’s hypotheses
which motivated the need for an exploratory research. Therefore, in Phase 2 we use an
inductive approach to explore motivations in S&Es: we start with a set of observations,
analyze them and test possible patterns (tentative hypotheses). This process let us build
the theory with which we answer our three research questions from a different
20
perspective. This last phase produces important contributions to the literature and practice
of management.
Figure 3-1. Overview of the research approach
3.1 Phase 1: Testing Assumptions about Scientists and Engineers in a Large
Dataset
3.1.1 Data and Sample
The goal of Phase 1 is to test old assumptions about scientists and engineers today.
For that reason, we chose to use a large data set that includes contemporary information
from S&Es in the US. More specifically, we use the Integrated Survey Data SESTAT
PUBLIC 2010 (National Science Foundation, National Center for Science and
Engineering Statistics, 2015), which is collected and managed by the National Science
Foundation and is available for public use. This data set combines information from three
2010 surveys (Survey of Doctorate Recipients, the National Survey of College Graduates,
Phase 1:
Testing old assumptions using statistical analysis
Phase 2:
Exploring motivations using grounded theory method
Ded
uctiv
e app
roach
Induct
ive
app
roac
h
Existing theory
Observation
Hypothesis
Substantive Theory
Tentative
hypothesis
Pattern
Observations
Literature
RQ1: What motivates scientists and engineers today?
RQ2: How do scientists and engineers respond to different incentives?
RQ3: How can this knowledge be used to improve incentives for scientists
and engineers?
Rejection of
hypothesis
Practice of management
Practical contributions
21
and the National Survey of Recent College Graduates), resulting in a total of 108,300
records. The data is weighted to represent the estimated population of S&Es in the U.S. in
2010 (26.9 million).
Scientists and engineers in this database are defined as individuals with an S&E-
related degree and/or occupation. The data set contains information on employment,
educational background and demographics of the respondents. To protect their identities,
some variables are recoded. For example, in the survey individuals indicate the specific
code that corresponds to their job code from a long list. The publicly available
information that can be obtained from the dataset provides a general code that aggregates
related job codes, not the specific one that each individual selected. Actually, there is no
public access to individual responses; only aggregated tables can be generated from the
SESTAT Data tool, an online platform that allows users to create their own data tables.
In this study, we created a database by repeatedly generating data tables from the
SESTAT data tool because it was not possible to generate a single table containing all the
information we needed from the website. This computational limitation is also the reason
why we had to strictly limit both the number of variables to study and the sample
population. Each table that was generated to integrate our database was set up to include
the aggregated responses of individuals that were working in the industry or government;
have a degree in Biology, Physics, Engineering, or Computer Science (we excluded
people with degrees in Social Sciences, related S&E degrees and unrelated S&E); and
work in a job code in these same set of areas. We also filtered by individuals who spent
most of their time doing basic research, applied research, development, design, computer
applications, or management (excluding finance, machine operation, and other activities)
22
and recorded their highest degree. These delimitations in our data make our results
generalizable to these specific employment sectors and professions and comparable to
similar studies in the literature.
From the set of people with the characteristics described above, we collected
responses to the following question: “Thinking about your principal job held during the
week of October 1, please rate your satisfaction with that job’s…” This question offered
a list of aspects of the work where people had to rate their job satisfaction. From this list
we specifically chose to study their responses on four facets of the work: salary,
opportunities for advancement, level of independence, and intellectual challenge. We also
collected their response to the question: “How would you rate your overall job
satisfaction with the principal job you held during the week of October 1, 2010?” Both
questions are measured on the following scale: very satisfied, somewhat satisfied,
somewhat dissatisfied, and very dissatisfied.
Table 3-1 displays all the variables and their corresponding levels used in this
research. Specifically, this table shows the description of each variable, their names, their
types, and possible response values. As we mentioned in the previous paragraph, job
satisfaction variables are measured on a 4-point scale. To facilitate the collection of the
data for our database, and because responses are clustered in the positive end of the scale
in all job satisfaction variables, we dichotomized the response scale to account for the
difference between being very satisfied (1) and less than very satisfied (0). In their study,
Sauermann & Stephan (2010) also dichotomized the 4-point response scale of the NSF
surveys.
23
Table 3-1. Variables in Phase 1
Description Variable
name
Type Values
Overall job
satisfaction ojs Dummy 1= very satisfied
0= somewhat satisfied, somewhat dissatisfied, or very
dissatisfied
Job satisfaction with
salary jss Dummy 1= very satisfied
0= somewhat satisfied, somewhat dissatisfied, or very
dissatisfied
Job satisfaction with
advancement jsa Dummy 1= very satisfied
0= somewhat satisfied, somewhat dissatisfied, or very
dissatisfied
Job satisfaction with
independence jsi Dummy 1= very satisfied
0= somewhat satisfied, somewhat dissatisfied, or very
dissatisfied
Job satisfaction with
challenge jsc Dummy 1= very satisfied
0= somewhat satisfied, somewhat dissatisfied, or very
dissatisfied
Job code jc Nominal 1= Computer Science 3= Physics
2= Biology 4= Engineering
Field of study of
highest degree fshd Nominal 1= Computer Science 3= Physics
2= Biology 4= Engineering
Work activity spent
most time on in
principal job
wa Nominal 1= Basic research 4= Design
2= Applied research 3= Development
5= Computer applications 6= Management
Highest degree hd Dummy 0= M.S. or B.S.
1= Ph.D.
3.1.2 Deductive Approach
Figure 3-2 represents the research approach used in the first part of this research.
First, to be able to set up our hypotheses, we develop a conceptual model from existent
literature. Then, we express the conceptual model in mathematical form (hypotheses) and
run analysis using a logistic regression model.
24
Figure 3-2. Research approach in Phase 1
As it can be seen on the top part of Figure 3-2, the conceptual model for predicting
overall job satisfaction (ojs) has four predictors: job satisfaction with salary (jss),
advancement (jsa), independence (jsi), and challenge (jsc). The bottom part of Figure 3-2
shows the specific hypotheses tested in this research. These hypotheses are tested using
different definitions (categorization criteria) of scientists and engineers. Phase 1’s
analysis ends with the acceptance or rejection of hypotheses.
The model used in Phase 1 is not meant to fully explain overall job satisfaction as this
construct can be driven by additional factors. Rather, our model is meant to serve as a
guide for understanding motivational preferences for specific incentives in a particular
population. For that reason, we focus on analyzing the relative strength of job
satisfaction’s predictors (βS, βA, βI, βC) for scientists and engineers. Additionally, we test
how our results change when defining scientists and engineers based on different
Independence
(jsi)
Salary
(jss)
Challenge
(jsc)
Advancement
(jsa) Overall job
satisfaction
(ojs)
βS
βA
βI
βC
Y (Response variable)
β (Coefficients)
X (Independent variables)
=
Level of satisfaction with:
Scientists: βI > βS and βI > βA and βI > βC
Engineers: βI < βS and βI < βA and βI < βC
Ded
uctiv
e app
roach
Conceptual model
Existing theory
Hypotheses
Acceptance or rejection of hypotheses
Test hypotheses using different categorization criteria (fshd, jc,
wa, and hd)
25
categorization criteria. This analysis let us answer the first research question (RQ1): what
motivates technical professionals today? Then, by calculating predicted probabilities with
our model we test how scientists and engineers would respond to different incentives
(RQ2). Lastly, we suggest ways in which our results can be used to improve incentives
for scientists and engineers (RQ3). In the following section we explain in more detail our
logistic regression model.
3.1.3 Logistic Regression
Logistic regression, also called logit, is a type of linear model appropriate for
situations where the response outcome is dichotomous. Since the relationship between
observed outcome and predictor is not linear, we create a “monotonic but nonlinear
transformation of the observed outcome” (Cohen & Cohen, 2003). The general logistic
regression equation for predicting the probability of being a case 3 �̂�𝑖 from multiple
predictors is given by Equation (1):
𝑝�̂� =1
1+𝑒−(𝐵0+𝐵1𝑋1+𝐵2𝑋2+⋯+𝐵𝑁𝑋𝑁) (1)
This equation can be expressed in different forms, such as in Equation (2) and (3).
𝑙𝑜𝑔𝑖𝑡 = 𝑙𝑛 (𝑝�̂�
1−𝑝�̂�) (2)
𝑙𝑛 (𝑝�̂�
1−𝑝�̂�) = 𝐵0 + 𝐵1𝑋1 + 𝐵2𝑋2 + ⋯ + 𝐵𝑁𝑋𝑁 (3)
In Equation (2) it can be seen that the logit function is the log of the odds of being a
case. In logistic regression, the coefficients are in terms of log odds. But because
3 Being a case means that the response variable corresponds to a particular outcome of a dichotomous
variable. For example, a case may represent getting heads when flipping a coin. In general, when the
response variable is binary, a case would be a 1 and a non-case a 0. It doesn’t matter which one of the two
outcomes is chosen to represent a case as long as the results are interpreted according to the variable chosen
to be the case.
26
interpreting coefficients in terms of log odds is not very intuitive, we transform the
coefficients to odds-ratios by calculating the exponential of the coefficient as shown in
Equation (4).
𝑂𝑅 = 𝑒𝐵𝑗 (4)
Remember that the odds are the ratio of the probability of something occurring (a
case) to the probability of the event not occurring (a non-case) (Powers & Xie, 2008). We
use odds-ratios (OR) to measure the relative likelihood of the odds of two outcomes. For
example, we run a logistic regression where the outcome variable is overall job
satisfaction (Y={1 if very satisfied, 0 if less than very satisfied}) and one of the
predictors is satisfaction with salary (X1={1 if very satisfied, 0 if less than very
satisfied}). With this model, what we observe is the group membership (overall very
satisfied versus less than very satisfied with job) but what we predict is the probability of
being in a group (being a case). Consider, for example, that the resulting OR for the
coefficient of X1 is 5, then we would interpret this number as follows: holding all other
predictors constant, the odds of being overall very satisfied with the job are 5 times
higher for a person who is very satisfied with salary than for a person who is less than
very satisfied with salary.
For the computation of the regression coefficients, goodness of fit, predicted
probabilities and graphs, we use the software STATA.
3.2 Phase 2: An Inductive Approach to Exploring Motivations of Scientists and
Engineers
As shown in the literature review section, the literature on scientists’ and engineers’
motivations needs to be revisited. So far, the methodological approach for studying
27
technical professionals has been mostly deductive, where explicit sets of theoretical
assumptions (hypotheses) are empirically tested. Such an approach has the limitation of
restricting the findings to what is measured. In this Phase of the research, we take an
inductive approach to answer our research questions. This approach does not use existing
theory but rather builds it from the data. The remainder of this chapter is dedicated to
explaining the research approach for the last part of our study.
3.2.1 Grounded theory method
Qualitative research is appropriate when the goal is to develop a theoretical
framework from the data (Babbie, 2010) that helps us make sense of the worlds we study
(Charmaz, 2006). Particularly for this part of the research we take an inductive, bottom-
up approach using grounded theory method (Glaser & Strauss, 1967) to understand how
technical professionals perceive their work as a basis for discovering their underlying
motivations.
Grounded theory requires simultaneously collecting data and analyzing it (Glaser &
Strauss, 1967). This iterative process is not easy to represent graphically. To be able to
communicate our data collection and analysis process, we created Figure 3-3.
28
Figure 3-3. Grounded theory method
Grounded Theory Method
Code set version 1
Code_1 Code_2
Code_3
.
.
. Code_N
Interview P1 transcription
Quotation 1.1 Quotation 1.2
.
.
.
Quotation 1.N
Interview guide:
Common set of questions
+ questions
about emergent
categories Next
interviewees
Sampling strategy:
Snowball sampling
Interview guide:
Common set of questions
(background,
preferred activities,
incentives) Next
interviewees
P1
P2 P3
Observation
Memo writing
Emergent categories
Code set version 2
Code_1 Code_2
Code_3
.
.
. Code_N
Interview P2 transcription
Quotation 2.1 Quotation 2.2
.
.
.
Quotation 2.N
Constant comparison:
Hypothesis testing of
emergent
categories
Observation
Memo writing
Final code set
Code_1 Code_2
Code_3
.
.
. Code_N
Interview PN transcription
Quotation N.1 Quotation N.2
.
.
.
Quotation N.N
Interview guide:
Common set of questions
+ questions
about emergent
categories
Emergent theory
P4 P5
PN
Stopping rule:
Saturated categories
Observation
Memo writing
Data collection method:
interviews and observation
Analysis: Grounded theory
method (coding, memo
writing constant comparison)
Output: Emergent theory
on technical professionals’
motivation
29
Circles in the left side of Figure 3-3 represent interviewees and their arrows illustrate
the sampling process (snowball sampling). The double-sided rectangles to the right of the
circles represent the set of questions (interview guide) used in each interview. After each
interview a transcript of the conversation was generated and the most relevant quotations
per interview were selected for analysis. From each quotation, a single or set of codes
was extracted. The multiple arrows going from transcription to code set represent this
step. Through a process of constant comparison of the codes, complemented with memos
and data from observation, emergent categories arise (rectangles at the far right of the
figure). The same process is repeated for each interview and the emergent codes and
categories are iteratively compared and analyzed with respect to the previous interviews.
Although there is a common guide for all interviews, emergent categories also inform and
guide the data collection process (arrows crossing the figure from right to left). The data
collection stops when theoretical saturation is reached. In the following sections, we will
explain in more detail each part of Phase 2’s research approach.
3.2.2 Research Context
Since our goal was to understand scientists’ and engineer’s motivations – particularly
the ones who work in a technical organization doing S&E-related activities – we needed
to find a research setting that employed a mixture of S&Es working on a range of R&D
and production projects. We were able to find these characteristics in a particular NASA
center.
This study was conducted on site at one of the ten NASA field centers. Unlike other
R&D government labs, NASA not only focuses on developing science, but also creates
and uses new technology. This characteristic makes NASA representative of other
30
engineering-enabled, large-scale science organizations (ex. CERN4 and some industrial
R&D laboratories). The particular center under study is responsible for activities
including research, design, manufacturing, integration, testing, operations of spacecraft
and instrumentation, and it employs a mix of engineers, scientists, and managers. The
specific business unit chosen within the center (purposive sample) is a good
representation of the R&D ecosystem because it covers research, technology
development, and production. Moreover, in this unit, it is possible to find a variety of
technical professionals who enjoy some level of discretion to working in some, or all, of
these activities.
Particular to the context of NASA, scientists are defined as professionals who are part
of the Science Division. A PhD with a post doc in Physics who works in the Engineering
Division is an engineer in the NASA culture, not a scientist. Although the selected
business unit does work for the NASA Headquarters Science Mission Directorate (SMD),
it exemplifies a key grey area where scientists and engineers work together to develop
incremental and revolutionary technologies. NASA is a matrix organization and SMD
draws its workforce from several divisions.
3.2.3 Sample
Interviewees ranged from relatively recent hires to senior employees, from managers
to scientists, from those with bachelor’s degrees to postdocs, people in different
hierarchical levels, and employees with the same background who prefer to work on
different parts of the R&D cycle. They were selected based on a diversity of preferences
for work and their willingness to voluntarily participate in this study. The first set of
4 The European Organization for Nuclear Research
31
interviews was arranged with the help of a couple of informants who identified people
from the specific unit described above. Our informants are professionals with over twenty
years of experience in the chosen organization, who are familiar with the context of our
study, and have strong internal connections.
Although an exploratory study is by nature open-ended, we defined an interview
guideline to keep us focused within the broad area that we are interesting in
understanding. Interviews were structured around three topics: background and career
progression, job characteristics and preferences, and incentives. This research received
GWU IRB approval (IRB#031337).
At the end of each interview, the interviewee was asked to contact people who would
have a different perspective/experience to offer to our research. This process is called
snowball sampling (Handcock & Gilet, 2011). To explore emerging categories a
combination of theoretical sampling (Glaser & Strauss, 1967) and snowball sampling was
used (Morse, 2010). The process of referral continued until theoretical saturation was
reached (Glaser & Strauss, 1967) additional respondents were not adding new insights to
the categories. The authors conducted four additional interviews to ensure that theoretical
saturation was reached.
3.2.4 Data Analysis
The data were collected from face-to-face, semi-structured interviews with 25
scientists and engineers at a NASA center. Interviews were structured around three
topics: background and career progression, job characteristics and preferences, and
incentives (see Appendix A). They lasted an average of 50 minutes. Interviews were
32
recorded, transcribed and then coded.5 ATLAS.ti, a coding software, was used to help
organize quotations and codes. In all, about 21 hours of interviews were transcribed. A
random number from 1 to 25 was assigned to each interviewee to be used as his/her
identifier in order to ensure anonymity in the data. In the following sections, when
recalling an interviewee’s quote, his/her identifier is displayed in parentheses at the end
of the quotation. This gives the reader the opportunity to trace quotes from particular
participants, demonstrates variety in quoting sources, and provides transparency and
consistency in the findings.
The basic datum in this study is a quotation. Each quotation was interpreted in an
iterative process that resulted in a set of codes (open coding). Codes represent the
relationship between data and theory and allow the researcher to conceptualize what is
being empirically observed (Holton, 2007). After quotations were broken down in
descriptive labels (codes) they were re-evaluated in terms of interrelationships and lastly
subsumed into broad classes (categories and dimensions) (Goulding, 2002). This constant
comparison process helps modify and/or improve the interpretation of the emerging
concepts and core categories. In the grounded theory method, data is analyzed as it is
collected, and the collection process stops when no new dimensions – or properties of
those dimensions – emerge from the data (theoretical saturation) (Glaser & Strauss, 1967;
Holton, 2007). In this research, we focused on coding motives (what motivates people)
and actions (what people do in relation to their motives). The following are examples of
quotations and its motive and action codes, respectively: “My favorite [part of my job] is
to see that we delivered things.” (P18) – Motive code: ‘Delivering’; “I use vacations but I
5 One respondent asked not to be recorded. Detailed notes were made instead, serving as a partial transcript.
33
don’t stop working on what I’m working on [while on vacation]” (P17) – Code: ‘Work
all the time (voluntarily)’.
We analyzed our data in two phases. First, we examined motive codes and their
interrelationships with the objective of identifying and organizing what motivates the
technical professionals. This part of the analysis allowed us to respond to our first
research question: what motivates scientists and engineers today? Our results are
presented in Figure 5-1, which displays the motive-based model. Then, we sorted the
people in our sample into the identities defined in the previous section and analyzed their
action codes. With this information, we labeled and described the identities found in our
sample in terms of their distinguishing action codes (Table 5-1). Understanding the core
motivations of people and their actions is our basis for theorizing about the link between
motivation and response to incentives, which help us respond to our RQ2. Finally, we
propose ways in which to improve incentives for scientists and engineers with different
motivations (RQ3).
In the following chapters we describe the analysis and findings of Phase 1 (Chapter 4)
and Phase 2 (Chapter 5). We complete this this dissertation with a chapter dedicated to
the conclusions of our study.
34
Effective incentives motivate people. Managers in technical organizations need to
effectively motivate their scientists and engineers with incentives that make sense to
them. Furthermore, in an environment of constrained resources, managers have to make
decisions about what incentives to prioritize and then offer them to the right people.
Incentives for technical professionals are based on assumptions about their preferences.
One of the motivational differences between scientists and engineers is how much they
care about independence. According to the literature, scientists highly rate independence
(Box & Cotgrove, 1968; Glaser, 1963; Pelz, 1967; Ritti, 1968) while engineers prefer
salary, advancement, and challenge to independence (Kerr et al., 1977; Raudsepp, 1963;
Ritti, 1968; Wilensky, 1964). In this chapter we explore current data, test the
aforementioned assumptions, and craft some implications for managers of scientists and
engineers based on our results.
4.1 Data description
As mentioned in the previous chapter, we use the SESTAT PUBLIC 2010 database,
which contains about 108,300 records of scientists and engineers. The available data is
weighted to represent the estimated population of S&Es in the U.S. in 2010 (26.9
million). For our study we only included information about people who have studied
science or engineering and who work in the industry or government (we exclude
academics). We also filtered by people who work in a science or engineering job code
and whose principal work activity, that is, the activity in which they spend most of their
35
time is either R&D, design, computer applications, or management (we exclude finance,
machine operators, and other work activities that are not of interest in this study). Table
4-1 summarizes the frequency, percentage, and cumulative percentage of our control
variables with their corresponding levels.
Table 4-1. Descriptive statistics: frequencies in descending order
Frequency Percent Cumulative
Total 2,310,110 100.00%
Field of study of highest degree
Engineering 1,115,947 48.31% 48.31%
Computer and mathematical scientists 777,688 33.66% 81.97%
Biological, agricultural and other life scientists 234,413 10.15% 92.12%
Physical and related sciences 182,062 7.88% 100.00%
Job code
Computer and mathematical scientists 1,101,015 47.66% 47.66%
Engineering 886,913 38.39% 86.05%
Biological, agricultural and other life scientists 195,160 8.45% 94.50%
Physical and related sciences 127,022 5.50% 100.00%
Principal work activity
Computer applications, programming, systems
development 790,673 34.23% 34.23%
Managing or supervising people or projects 458,098 19.83% 54.06%
Design of equipment, processes, structures, models 404,756 17.52% 71.58%
Applied research 295,153 12.78% 84.35%
Development 292,011 12.64% 96.99%
Basic research 69,419 3.01% 100.00%
Highest degree
Non_PhD 2,099,114 90.87% 90.87%
PhD 210,996 9.13% 100.00%
From Table 4-1 it can be seen that the majority of professionals in our sample are
concentrated in the areas of engineering and computer science both as their highest
degrees and job codes. Work activities in R&D (basic research, applied research, and
development) represent 28% of all work activities, while the most popular is computer
applications (48%). Lastly, a large majority of S&E professionals (91%) do not have
PhDs.
36
The job satisfaction variables used in this study are described in Table 4-2 along with
their frequencies and percentages. About 58% of the sample declares being less than very
satisfied with their jobs overall. With respect to job satisfaction with specific facets of the
work, advancement is the area where people are mostly less than very satisfied (79%) and
independence is where the majority are very satisfied (57%). In Table 4-3 we can see that
all these variables are moderately correlated.
Table 4-2. Descriptive statistics: job satisfaction
Job satisfaction (JS) Less than very
satisfied
Percentage
less than
very
satisfied
Very satisfied
Percentage
very
satisfied
Overall JS 1,339,230 57.97% 970,880 42.03%
JS with salary 1,501,710 65.01% 808,400 34.99%
JS with opportunities for advancement 1,814,382 78.54% 495,728 21.46%
JS with level of independence 999,578 43.27% 1,310,532 56.73%
JS with intellectual challenge 1,349,461 58.42% 960,649 41.58%
Table 4-3. Tetrachoric correlations
Variable 1 2 3 4 5
1 Overall JS 1
2 JS with salary 0.6449 1
3 JS with opportunities for advancement 0.682 0.4366 1
4 JS with level of independence 0.6197 0.3399 0.5654 1
5 JS with intellectual challenge 0.6782 0.4082 0.6849 0.5998 1
Table 4-4 and Table 4-5 display our data in the form of contingency tables using all
our categorization variables and overall job satisfaction. The first numeric cell of the
table indicates the number of non-PhD professionals doing basic research in a computer
science job code, who studied computer science and who are less than very satisfied with
37
Table 4-4. Contingency table with percentages, part 1
Work activity Basic research Applied research Development
Jc Fshd Ojs NoPhD % PhD % NoPhD % PhD % NoPhD % PhD %
CS
CS Less 2,618 55% 141 23% 21,538 65% 4,202 62% 36,089 64% 1,324 55%
Very 2,126 45% 477 77% 11,602 35% 2,565 38% 20,058 36% 1,063 45%
Bio Less 561 92% 36 39% 624 29% 752 76% 2,950 77% 90 54%
Very 49 8% 57 61% 1,495 71% 244 24% 889 23% 77 46%
Phy Less 53 84% 156 74% 746 84% 348 86% 388 23% 255 63%
Very 10 16% 56 26% 144 16% 59 14% 1,334 77% 148 37%
Eng Less 1,465 79% 77 43% 6,020 56% 1,398 63% 15,341 61% 1,258 67%
Very 381 21% 104 57% 4,754 44% 813 37% 9,988 39% 606 33%
Bio
CS Less 35 100% 0% 488 31% 10 100% 0%
Very
0% 8 100% 1,067 69%
0%
20 100%
Bio Less 4,480 31% 8,144 58% 27,755 61% 12,006 52% 6,739 44% 3,316 56%
Very 10,167 69% 5,793 42% 17,605 39% 11,239 48% 8,507 56% 2,607 44%
Phy Less 864 57% 845 37% 2,107 56% 1,416 34% 769 52% 1,061 50%
Very 663 43% 1,429 63% 1,640 44% 2,752 66% 708 48% 1,067 50%
Eng Less 354 100% 325 90% 554 51% 277 36% 446 77% 422 75%
Very 0% 36 10% 525 49% 495 64% 130 23% 138 25%
Ph
y
CS Less
0% 91 45% 38 100% 58 100%
Very
114 100% 110 55%
0%
0%
Bio
Less 315 55% 61 44% 6,639 79% 393 60% 2,236 67% 263 72%
Very 254 45% 77 56% 1,799 21% 267 40% 1,106 33% 101 28%
Phy Less 3,165 52% 1,701 33% 15,275 55% 9,641 63% 5,037 49% 4,789 70%
Very 2,913 48% 3,420 67% 12,281 45% 5,631 37% 5,229 51% 2,085 30%
Eng Less 34 5% 265 57% 3,604 62% 1,002 58% 1,016 65% 634 79%
Very 669 95% 204 43% 2,187 38% 735 42% 557 35% 167 21%
En
g
CS Less 899 100% 2,348 56% 28 16% 2,830 64% 185 53%
Very
0%
1,862 44% 145 84% 1,621 36% 165 47%
Bio Less
30 100% 1,010 26% 322 39% 583 47% 86 49%
Very
0% 2,941 74% 505 61% 648 53% 89 51%
Phy Less 1,427 98% 61 2% 2,706 81% 1,130 29% 3,621 84% 2,141 43%
Very 23 2% 2,646 98% 619 19% 2,778 71% 686 16% 2,850 57%
Eng Less 6,775 63% 855 54% 32,862 49% 8,720 57% 71,590 61% 11,721 68%
Very 3,916 37% 731 46% 33,737 51% 6,639 43% 46,655 39% 5,546 32%
38
Table 4-5. Contingency table with percentages, part 2
Work activity Design Comp. Apps Management
Jc Fshd Ojs NoPhD % PhD % NoPhD % PhD % NoPhD % PhD %
CS
CS Less 28,984 57% 1,125 81% 282,955 59% 4,993 58% 61,836 61% 1,072 60%
Very 22,156 43% 266 19% 195,130 41% 3,688 42% 39,182 39% 703 40%
Bio Less 628 65% 55 73% 10,330 59% 1,363 65% 929 29% 154 39%
Very 335 35% 20 27% 7,118 41% 718 35% 2,259 71% 246 62%
Phy Less 546 38% 301 70% 13,994 68% 2,913 72% 2,096 68% 428 74%
Very 899 62% 132 30% 6,442 32% 1,152 28% 985 32% 150 26%
Eng Less 8,297 57% 740 69% 115,767 64% 4,460 79% 24,926 59% 952 80%
Very 6,354 43% 339 31% 66,300 36% 1,175 21% 17,661 41% 232 20%
Bio
CS Less 33 100% 0% 0% 0%
Very 0% 17 100%
10 100% 87 100%
Bio
Less 2,763 91% 118 43% 246 5% 235 82% 15,505 45% 3,375 56%
Very 258 9% 154 57% 4,861 95% 52 18% 19,225 55% 2,609 44%
Phy Less 237 100% 75 38%
1,211 93% 580 33% 551 39%
Very 0% 123 62%
95 7% 1,164 67% 880 61%
Eng Less 342 92%
185 100% 27 23% 630 70% 82 71%
Very 31 8% 0% 92 77% 265 30% 33 29%
Ph
y
CS Less
6 100% 0% 30 100% 44 6%
Very
0% 55 100%
0% 751 94%
Bio
Less 166 58%
0% 10 100% 49 63% 4,331 72% 176 50%
Very 121 42% 85 100% 0% 29 37% 1,659 28% 179 50%
Phy Less 668 36% 901 62% 894 72% 562 61% 6,167 51% 2,148 61%
Very 1,173 64% 555 38% 348 28% 355 39% 5,810 49% 1,358 39%
Eng Less 156 58% 68 69%
25 16% 748 52% 165 64%
Very 115 42% 30 31%
132 84% 699 48% 91 36%
En
g
CS Less 5,504 73% 42 12% 3,603 69% 116 57% 4,076 78% 85 77%
Very 2,050 27% 295 88% 1,591 31% 88 43% 1,164 22% 26 23%
Bio Less 773 26% 217 88% 167 100%
2,309 51% 155 75%
Very 2,181 74% 29 12% 0%
2,261 49% 53 25%
Phy Less 2,513 89% 700 64% 2,143 96% 141 3% 2,434 70% 876 100%
Very 323 11% 400 36% 99 4% 4,623 97% 1,044 30%
0%
Eng Less 165,216 56% 7,054 64% 28,547 55% 1,637 57% 114,447 55% 4,092 58%
Very 131,678 44% 3,959 36% 22,897 45% 1,243 43% 94,372 45% 2,958 42%
39
their jobs. The 2,618 people in that cell represent a 55% of the total for that category.
Immediately below that cell, there is the other 45%, which represents the people with the
same characteristics but who are very satisfied.
Table 4-4 and Table 4-5 provide a detailed summary of our data with respect to the
response and control variables. However, to be able to further explore these data and
make predictions using the independent variables defined in our conceptual model, we
developed a logistic regression model. Our model predicts job satisfaction in S&Es based
on their personal characteristics and satisfaction with different facets of the work. This
model allows us to test old assumptions about this workforce in a large and current
dataset.
4.2 Inferential statistics
As mentioned in the previous chapter, we created a logistic regression model that
predicts overall job satisfaction in scientists and engineers. We used four predictors: job
satisfaction with salary, advancement, independence, and challenge. We control for field
of study of highest degree, job code, work activity, and highest degree. This last set of
variables is used to categorize people as scientists or engineers and we test our results
with respect to different categorizations of professionals. For the complete details on the
regressions’ results and goodness of fit see Appendix B. Since our focus is on
understanding differences between types of professionals, we not only analyze predicted
probabilities but we also explore differences in coefficients and differences in results
when varying the categorization criteria.
The logistic regression model for all data predicts 34% of the variation (pseudo R2).
The full model and parameters are all significant at the 0.5% level. Since coefficients in
40
logistic regression are not easily interpretable because they are measured in log odds, we
transform the coefficient to odds-ratios. Table 4-6 displays the odds-ratios for each
predictor as well as their 95% confidence intervals. The odds-ratios corresponding to the
covariates are all significant at the 5% but they are all close to one, which means that they
are not strong predictors.
Table 4-6. Rank of odds-ratios for logistic regression with all data with alpha 0.5
These odds-ratios were interpreted using the following example: the chances of being
very satisfied with work are 5.5 higher if he/she is very satisfied with his/her salary with
respect to someone who is less than very satisfied with salary, with all other things being
equal. Likewise, someone who is very satisfied with his/her level of independence is 3.4
times more likely to be very satisfied with their job than someone who is less than very
satisfied with independence. Based on the ranking of odds ratios, S&Es have higher
chances of being satisfied with their jobs if they are satisfied with salary, advancement,
challenge, and independence, in descending order of impact.
Our model predicts overall job satisfaction for all S&Es but to be able to test old
assumptions about them we need to control for different types of employees and find out
whether some facets of the work are actually more valued than others for certain
professionals. In particular, and based on our literature review, we test whether we find
evidence or not for the following (nonparametric) hypothesis:
Rank Odds-
ratio Predictor 95% C.I.
1 5.5 Salary [5.4, 5.5]
2 3.7 Advancement [3.7, 3.8]
3 3.6 Challenge [3.6, 3.7]
4 3.4 Independence [3.4, 3.4]
41
a. Independence, compared to salary, advancement, and challenge, is the
strongest predictor (top rank) of scientists’ job satisfaction.
b. Independence, compared to salary, advancement, and challenge, is the
weakest predictor (low rank) of engineers’ job satisfaction.
To test these classic assumptions, we use classic definitions of scientists and
engineers. Specifically, we define scientists as people who have PhDs in science and
work in basic or applied research while engineers are the ones who studied engineering
and work in development or management.
In the following section we test these hypotheses and explore different categorization
criteria to see how sensitive our results are to the way we define S&Es.
4.3 Testing old hypotheses
The way we test the hypothesis presented in the previous section is by comparing the
value and ranking of the resulting odds-ratios in logistic regressions when controlling for
type of professional (scientist or engineer).
Table 4-7 presents the results of the regression for scientists defined as people who
have PhDs in science (biology or physics) and work in basic or applied research. This
table shows that the highest ranked predictor of job satisfaction for scientists is not
independence but rather salary. Actually, independence ranks third in strength of
prediction, after challenge and before advancement.
Table 4-7. Logistic regression result in odds-ratios for scientists
Rank Odds-
ratio Predictor 95% C.I.
1 7.4 Salary [7.1, 7.8]
2 5.1 Challenge [4.8, 5.3]
3 4.5 Independence [4.3, 4.7]
4 2.6 Advancement [2.5, 2.8]
42
Table 4-8 displays the results of the regression for engineers (defined as people who
studied engineering and work in development or management). This table shows that
independence is not the weakest predictor. Same as with scientists, the weakest predictor
is advancement. For engineers, independence and challenge are both in second place
(there is an overlap in their confidence intervals) following salary as the strongest
predictor.
Table 4-8. Logistic regression results in odds-ratios for engineers.
*indicates overlap in the confidence intervals. In other words, the coefficients are not significantly different therefore
they should be considered to be in the same ranking position.
These specific results do not support our hypotheses of independence being the
strongest predictor to scientists and weakest predictor to engineers. Next, we explore
different combinations of criteria for categorizing professionals to see whether our results
are a matter of better defining our sample (using a different combination of criteria) or
whether they are actually stable to all scientists and engineers.
As mentioned before, there are four variables in our dataset that can be used to define
professionals: field of study of highest degree, job code, work activity, and highest
degree. Each one of these criteria has multiple levels: 4, 4, 6, and 2, respectively. Thus,
there are 192 possible combinations of levels that could be explored. It is computationally
challenging to explore all these combinations; therefore, we use one criterion at a time
and let those results guide subsequent searches. In the following section we explore
Rank Odds-
ratio Predictor 95% C.I.
1 6.3 Salary [6.2, 6.4]
2 3.1 Independence [3.0, 3.1]*
3 3.0 Challenge [2.9, 3.0]*
4 2.8 Advancement [2.8, 2.9]
43
different combinations for categorizing scientists and engineers to understand how ranks
change depending on the way they are defined.
4.4 Selecting key variables for detailed follow-up
In this section we analyze the odds-ratios ranks of the logistic regressions results
using one criterion at a time. The results in this section are the basis for the groupings of
criteria that are used in the next section.
Table 4-9 includes the results for the regressions using one criterion at a time. This
table shows that in general, satisfaction with salary is the strongest predictor of overall
job satisfaction when using single categorization criterion. The classic scientist (in terms
of highest preference for independence) can only be found in people who studied biology
and people who work in a biology job code. The classic engineer (in terms of lowest
preference for independence) can be found in people who studied engineering or
computer science, people who work in a job code that is not biology, and professionals
mainly doing management or computer applications.
Classifying professionals by their field of study of highest degree or job code result in
similar rankings of incentives between criteria. To understand why this is happening, we
look at the number of people by discipline in each job code. Table 4-10 shows that most
professionals work in a job code that corresponds to their field of study of highest degree.
For this reason, in the following section we only use field of study of highest degree
when combining criteria. Moreover, to be able to see more differentiated results we will
use biologists and engineers instead of the four classifications within field of study of
highest degree.
44
Table 4-9. Odds-ratio ranking for each criterion
By Field of Study of Highest Degree
RANK Engineering Comp. Science Physics Biology
1 Salary (5.6) Salary (6) Salary (5.9) Independence (6.6)
2 Challenge (3.4) Advancement (5.4) Independence (4.5) Salary (4.1)
3 Advancement (3.1) Challenge (3.7) Challenge (4.1) Challenge (3.9)
4 Independence (3.1) Independence (2.9) Advancement (3.1) Advancement (3.4)
By Job Code
RANK Engineering Comp. Science Physics Biology
1 Salary (5.2) Salary (6.1) Salary (5.8)* Independence (7.1)
2 Challenge (3.6) Advancement (4.1) Challenge (5.5)* Salary (4.2)
3 Advancement (3.4) Challenge (3.3) Advancement (5.1) Challenge (3.8)
4 Independence (3) Independence (3.2) Independence (4.5) Advancement (3.5)
By Work Activity
RANK Management Comp. Apps Design
1 Salary (4.1) Salary (5.9) Salary (4.8)
2 Advancement (3.8)
Advancement
(4.2)* Advancement (3.9)
3 Challenge (3)* Challenge (4.1)* Independence (3.4)
4 Independence (2.9)* Independence (3) Challenge (2.9)
By Work Activity
RANK Development Applied Research Basic Research
1 Salary (5.7) Salary (7.1) Salary (7.7)
2 Independence (4)* Independence (4.5) Challenge (6)
3 Challenge (3.9)* Challenge (4.3) Independence (5.3)
4 Advancement (2.8) Advancement (3.7) Advancement (2.1)
By Highest Degree type
RANK Non-PhD PhD
1 Salary (5.3) Salary (6.1)
2 Advancement (3.7) Challenge (4.8)
3 Challenge (3.5) Independence (3.5)*
4 Independence (3.3) Advancement (3.3)*
*indicates overlap in the confidence intervals. In other words, the coefficients are not significantly different therefore
they should be considered to be in the same ranking position.
Table 4-10. Contingency table for field of study by job code
Job code
CS Bio Phy Eng Total
Fsh
d
CS 95.9% (745,893) 0.2% (1,775) 0.2% (1,297) 3.7% (28,723) 777,688
Bio 13.6% (31,979) 71.6% (167,759) 8.7% (20,316) 6.1% (14,359) 234,413
Phy 18.5% (33,735) 11.1% (20,237) 50.6% (92,106) 19.8% (35,984) 182,062
Eng 25.9% (289,408) 0.5% (5,389) 1.2% (13,303) 72.4% (807,847) 1,115,947
When using the work activity criterion, our results suggest a division that could be
mapped to the scientist versus engineer dichotomy. On the one hand, individuals doing
45
basic research, applied research, or development show similar preferences: salary is the
strongest predictor for job satisfaction while advancement is the weakest. On the other
hand those doing design, computer applications, or management rank salary and
advancement higher than independence and challenge. This finding may indicate that
grouping people by a sub-criterion of work activity (R&D versus non-R&D) would result
in similar outcomes. Thus, in the next section we use R&D (basic research, applied
research, or development) versus non-R&D (design, computer applications, or
management) when referring to the work activity classification.
Finally, when using the highest degree criterion both PhDs and non-PhDs show
independence in the low end of the rank.
The results of this section indicate that we should explore a combination of criteria
for R&D versus non-R&D in biologists versus engineers.
4.5 Comparing preference for incentives using multiple criteria
Table 4-11 displays the odd-ratios and rank for the combined criteria defined in the
previous section (Appendix C contains a bigger set of tables with compared criteria). Our
results show that for both engineers (defined as people who studied engineering and who
works in non-R&D activities) and scientists (defined as people who studied biology and
who works in R&D activities), salary is the best predictor of job satisfaction. The rest of
the ranks look different when compared against each other. Engineers rank independence
last and biologists rank independence directly after salary.
Table 4-11. Results of logistic regression for combined criteria
Combined criteria
RANK Eng + Non R&D Bio + R&D
1 Salary (5.4) Salary (6.1)
2 Challenge (3.1) Independence (4.7)
3 Advancement (3.1) Challenge (3.6)*
4 Independence (2.9) Advancement (3.5)*
46
So far we have looked at the rank of the odds-ratios for the logistic regressions of
scientists and engineers defined by different criteria. Our results are all statistically
significant with the exception of some odds-ratios where there is no difference in rank
order, that is, where there is overlap in the confidence intervals. Although our results
could be considered enough evidence to claim that different professionals care about
different aspects of the job depending on their background and occupation, the statistical
significance of our results should not be interpreted as practically significant. To test
whether our results are significant to the practice of management, we look at the
predicted probabilities of job satisfaction for scientists and for engineers. According to
the literature, we expect to find that independence makes a big difference in the predicted
probability of job satisfaction for scientists, and the opposite for engineers. However,
according to our last results (Table 4-11), we would expect salary to have the biggest
impact on job satisfaction and independence/advancement to have the weakest impact on
47
engineers/scientists, respectively.
Figure 4-1 illustrates the predicted probabilities for job satisfaction in engineers (by
field of study of highest degree) by work activity, and Figure 4-2 presents the predicted
probabilities for job satisfaction in scientists (biologists by training). These two graphs
demonstrate that some combinations of predictors are stronger than others. For example,
engineers who are very satisfied with all facets but independence (yellow line in Figure
4-1) have a high probability of being very satisfied with their job (around 80% in all work
activities). However, the same happens if advancement is the only facet where they are
less than very satisfied (represented by a line under the yellow one, not visible and not
statistically different). But when looking at salary, it can be seen that the probability of
being less than highly satisfied drops noticeably (from around 80% to 70%) when salary
is the single facet of the work where the engineer is not very satisfied. This can be
interpreted as salary being the strongest predictor (consistent with our previous findings
48
and contrary to some of the classic depictions of engineers). Consequently, by looking at
the predicted probabilities when there is only one satisfied facet of the work, it is clear
that when that facet is salary the probabilities of being satisfied with the job are higher
than when other facets are the ones where there is satisfaction (light brown line).
Figure 4-1. Adjusted predictions of job satisfaction for engineers
In Figure 4-2 we observe a similar pattern but instead of salary being the strongest
predictor, it is independence. These results indicate that engineers care more about salary
whereas biologists care more about independence.
49
Figure 4-2. Adjusted predictions of job satisfaction for biologists
According to our previous analysis we expected to see higher predicted probabilities
in (1) engineers doing non-R&D and who are satisfied with aspects other than
independence and (2) scientists doing R&D who are satisfied with independence. These
statements are partially true. We did not find support for R&D/non-R&D being a better
criterion of categorization for scientists/engineers. Actually, both Figure 4-1 and Figure
4-2 show the same patterns of preference across work activities. Management shows
higher probabilities of job satisfaction than the rest of the work activities in both
engineers and biologists. To see the specific numbers in these figures see Appendix D.
Appendix E contains more graphs for predicted probabilities.
4.6 Discussion
The first part of our study presented some descriptive statistics in our sample that
showed us work characteristics and job satisfaction of scientists and engineers’ today. To
be able to draw conclusions about trends and patterns in the data, we built a logistic
50
regression model. We tested the classic assumptions of (a) scientists favoring
independence over other incentives and (b) engineers having the opposite preference. We
did not find support for the old assumption about preference for independence in either
scientists or engineers, as defined in the old literature (Section 4.3). Then, we tested
different criteria for categorization and found that the results of motivational preferences
were sensitive to the way we define scientists and engineers. On the one hand, we found
that the criteria of field of study of highest degree resulted in different ranks in all
disciplines; thus, we chose to explore combinations of criteria using only people who
have studied engineering to represent the engineers, and people who have studied biology
to represent the scientists. On the other hand, when using work activity as the
categorization criteria we found that a distinction was possible: separating R&D versus
non-R&D activities resulted in two groups of people with similar motivational
preferences, suggesting that this could be a criteria on which to base the scientist versus
engineer distinction. However, when combining these criteria (field of study of highest
degree and work activity), we found that work activity did not change the predicted
probabilities of job satisfaction in either engineers or scientists (biologists).
These results let us answer our first research question: what motivates scientists and
engineers today? Consistent with the literature, we confirmed that salary, advancement,
independence, and challenge are all significant motivators for scientists and engineers. In
particular, scientists (defined as people who have studied biology) feel more strongly
about independence than engineers (defined as people who studied engineering).
Our second research question is: how do scientists and engineers respond to different
incentives? Although our results point at the direction of independence/salary being
51
strong motivators to scientists/engineers, they are actually not stronger than the quantity
of satisfied facets. Specifically, we found that the probability of job satisfaction increases
more as a function of the amount of satisfied facets of the work than as a function of the
specific facets. In other words, quantity is more important than quality. For instance, an
engineer who is only very satisfied with salary (one satisfied facet) has lower
probabilities of being very satisfied with the job than if he was very satisfied with
challenge and independence (two satisfied facets). Hence, the power of salary as a
motivator for engineers is considerable only when compared to other motivators in the
same quantity of satisfied facets. This same effect happens to biologists.
Finally, how can we use this knowledge to improve incentives for scientists and
engineers? Our results suggest that managers need to focus on having their personnel
satisfied with as many facets of the work as possible. And if they want to be more
effective, they should focus on the motivators that their personnel most care about:
independence for scientists (biologists) and salary for engineers.
In conclusion, in this analysis we found that some of the old assumptions about
scientists and engineers are still valid today but that their practical implications are weak.
Specifically, we found that their preferences are different but not strong enough to justify
differentiated management practices for scientists and engineers. In other words,
management could assume that S&Es are a homogeneous group. This takeaway is not
satisfying given all the literature there is about S&Es’ preferences. Our results call for a
deeper analysis in the area of motivation of technical professionals. Thus far, we have
been analyzing and drawing conclusions from a limited number of motivators but what if
there are other motivators that are not being measured that may explain differences
52
better? Perhaps there are other areas that would actually increase people’s satisfaction
with work and that could be more informative to management. To explore this, we take
an inductive approach and develop a qualitative study that let us answer our research
questions from a different perspective.
53
This chapter presents an inductively developed model of the variety of technical
professionals’ motivations and the set of identities that they form. According to our
results from the analysis of the interviews, motivations can be described in terms of three
core dimensions: the social orientation, temporality of reward, and involvement with
technology. Each dimension is a spectrum with two distinctive ends (categories):
individual and relational, continuous and discrete, and direct and indirect, respectively.
As illustrated in the top portion of Figure 5-1, we inductively obtained categories and
dimensions by studying motive codes and their relationships. Then, we theoretically
organized the set of identities that all the possible combinations of our categories yields.
These core identities are displayed in the table at the bottom left of Figure 5-1. Finally, in
a second round of analysis (bottom right of Figure 5-1), we examined common codes by
core identity, which allowed us to theorize about scientists’ and engineer’s work
identities. This chapter describes each part of the model, from the dimensions to the
identities found in our sample. Throughout this whole chapter we illustrate our argument
with quotations either within the text and/or in footnotes.7
6 Most of the content in this chapter was recently published by the author in an IEEE Transaction of
Engineering Management article (Bignon & Szajnfarber, 2015). 7 All quotations were analyzed within the context of the whole interview, thus, the way we classified the
quotations were internally consistent with the whole tone of the interview.
54
Figure 5-1: Model of Motivations and Incentives
Core
Identities Social orientation
Temporality of
reward
Involvement
with
technology
(1) Individual Discrete Direct
(2) Individual Discrete Indirect
(3) Individual Continuous Direct
(4) Individual Continuous Indirect
(5) Relational Discrete Direct
(6) Relational Discrete Indirect
(7) Relational Continuous Direct
(8) Relational Continuous Indirect
Motive codes
Dimensions
Categories
Social orientation Temporality of reward Involvement with technology
Influencing
technical work
Doing hands-on
technical work
Contributing
to science
Understanding
phenomena Interacting Doing creative
work
Having
independence
Working
with others
Delivering
Seeing
things fly Facilitating
collaborations
Work on
what I want
Helping
others
Getting
things done Learning
Finding
opportunities
Working on new
technical problems Doing own
work
Direct Indirect Discrete Continuous Relational Individual
P1
P2
P3
PN
Analysis of common action codes
Sorting sample by
identity
(8) Bridgers
Relational – Continuous – Indirect
• Using people as resources
• Getting exposure to technology
(1) Intrapreneurs
Individual – Discrete – Direct
• Championing own work
• Learning to increase capacity for work
(3) Researchers
Individual – Continuous– Direct
• Seeking independence
• Finding time to work on what I want
(6) Enablers
Relational – Discrete – Indirect
• Keep things moving
• Knowing what others are doing
RQ1: What motivates scientists and engineers today?
RQ2: How do scientists and engineers respond to different incentives?
RQ3: How can this knowledge be used to improve incentives for scientists and engineers?
55
5.1 Dimensions of motivation
5.1.1 Social orientation
The social dimension refers to the individual’s interest with others in the context of
work. There are people who feel most excited when they work in interactive and
collaborative environments. Other people feel that too much interaction slows down their
work. Being individually oriented does not mean being asocial. What excites those who
are individually oriented is to have autonomy, to be able to do their own, independent
work. To individually oriented people, interaction is useful – it is a good resource for
finding better and faster solutions to the problems they are interested in solving – but not
necessarily enjoyable. We identify two different extremes of the social dimension: one
driven by relational motives and another driven by individual motives. An illustrative
example of a person motivated by relational aspects of the work is P11, who in the
quotation below expresses how he enjoys solving a technical problem in the context of a
team effort as opposed to working in isolation.
“My favorite part is also when I am part of the bigger team […] You do an optical design and
interact with the mechanical designers to package it. Does it fit? If not then we have to change it
so it's kind of a more team group effort. Myself, I enjoy that.” (P11) – Motive code: ‘Working
with others’
A counterexample is P17, who acknowledges the importance of interaction but
emphasizes the need for seclusion when thinking about the problems he wants to solve.
“The interaction is important, you definitely need that but you don’t need it as much probably. I
don’t need it as much […] to really dig deep in this problem, you have to become almost like a
monk. You have to be isolated, you have to have time to really think clearly and solve those
[problems]. It’s a state of mind that you are in that allows innovations to come. It’s a very fragile
state of mind.” (P17) – Motive code: ‘Independence’
56
5.1.2 Temporality of reward
This dimension refers to the timing of motivation with respect to work satisfaction.
Some individuals feel motivated when accomplishing specific goals. The biggest
satisfaction for these individuals arises in the discrete moment of task completion. Others
take pride in the continuous process of working towards those goals and feel motivated
over the course of the whole process. When the motivation comes from the process of
doing something such as working in the lab, learning, working on challenging problems,
forming networks, etc., we call it continuous. For this type of motive, deadlines do not
factor in as long as the process moves forward. Flexible environments are enjoyable. On
the other hand, when the sense of satisfaction is achieved once a goal is reached or a
product delivered, the motivation is called discrete. In this case, it is preferable to have
more structured work and goals. In the quotations below we can see two contrasting
views of the same phenomenon: the satisfaction of working on flight projects and R&D
varies depending on the individual’s temporality of reward. P24 enjoys flight projects
because there is certainty in the deliverable, whereas P1 enjoys working on technology
development independent of its completion time. Both P24 and P1 feel proud when
something flies but for one to see things fly is the discrete goal, and for the other it is a
nice byproduct of a continuous effort.
"The technology development work [is] hard, sometimes [it’s] not even possible […] and the time
frames are long so it takes much longer steps to make much slower progress […] [T]he flight
project is more satisfying just because you have a beginning and an end, it’s a relatively short
period of time, you can see an end product that is delivered.” (P24) – Motive code: ‘Deliver’
"[I]t's nice to actually have things flying that you worked on that I think there's a balance but I
much rather work on delivering a small component than managing some massive effort like I’m
doing now, so yeah, I much rather get into kind of smaller R&D and developing the next
generation of let's say laser instruments or something as opposed to trying to do the production
version of it. To me it's a lot more rewarding and it's a lot more…. You can make a lot more
progress per level of effort.” (P1) – Motive code” ‘Developing technology’
57
5.1.3 Involvement with technology
This dimension indicates the individual’s motivation for work with respect to its
connection with technology. When what brings more satisfaction is the hands-on,
technical work, we call it a direct motive. On the other hand, when motivation comes
from enabling technology without necessarily working on it, we call the motive indirect.
For example, in the quotations below P14 and P1 have different views on work: P14, who
had a technical role in the past, loves technology but prefers to do indirect work whereas
P1 feels most8 motivated when finding time to do direct technical work.
“I like being in the role that I’m in because I still get to stay close enough to the technical work,
although I’m not actually doing it anymore, and I have a chance to actually influence the culture
and really watch the people develop and play a role in that.” (P14) – Motive code: ‘Influencing
work’
“It’s a constant struggle to actually keep carving out time to really do research, what I call the real
work than instead of just managing other people.” (P1) – Motive code: ‘Hands-on technical work’
In summary, there are three dimensions that resulted from the process of interpreting
our qualitative data and abstracting their core concepts. The social orientation dimension
includes motives related to work interaction (individual or relational); the temporality of
reward dimension refers to motives that indicate the timing of satisfaction derived from
work (discrete or continuous); and lastly, the involvement with technology dimension
comprises motives associated with the nature of the work (direct or indirect technical
work).
5.2 Work identities
Each interviewee was evaluated with respect to his/her dominant codes. For example,
a person was classified as core identity (1) (see table within Figure 5-1) if his/her
8 Notice that we say most and not only motivated because as we mentioned before, these dimensions are
spectrums that we code by dominance for simplicity purposes.
58
responses to the interview questions contained predominantly individual, discrete, and
direct motive codes. After sorting all interviewees in our sample into their corresponding
identities, we analyzed their dominant action codes. Action codes are labels assigned to
quotations that refer to the individual’s behavior with respect to incentives. Our results
indicated that our entire sample could be classified into five of the eight possible core
identities. We then analyzed the action codes within and across the groups of individuals
in four of the five identities found in the sample. The fifth identity was not analyzed
because only one person was classified in that identity, making it impossible to single out
a distinctive behavior of that identity.
In this section we characterize the core identities found in our sample with respect to
their motive codes and common action codes. Table 5-1 summarizes these findings and
provides examples of quotations that describe behaviors of four of the core identities
found in our sample.
Table 5-1. Dominant action codes by identity (Bignon & Szajnfarber, 2015)
Identity found
in sample9
Dominant action
codes Quotation examples
(1) Individual –
Discrete –
Direct:
“The
Intrapreneurs”
Marketing own
technology
Finding
resources for
own projects
“You have to kind of market what you're doing to get the
necessary people…There is no other way because they don't care,
they got other things to worry about, I don't blame them.
Everyone has their own problems to work on but you have to
convince people that [your project] is critical” (P17)
“It’s not always fun to [write proposals]. Every single year I have
to compete to get funding for my work, if I don’t get funding for
my work then the work stops. There’s a long-drawn-out to
competitive process of getting funds so that’s a distraction but I
understand. There are a thousand scientists and engineers
wanting to do things…” (P7)
9The numbers in parentheses in the first column of this table are a reference to the identities in the table
within Figure 6
59
(3) Individual –
Continuous –
Direct: “The
Researchers”
Seeking
independence
Work all the
time
“If I get these two sources of funding [that I’m applying for] I
think I’ll be able to become more independent: have a team
working on some detector technology that nobody else is
working on in this branch.” (P13)
“It's not the money or whatever that's stopping us from having a
vacation, most of us don't go on vacations because we don't want
to take the time away from work to do it.” (P1)
(6) Individual –
Discrete –
Indirect: “The
Enablers”
Seeking
supervisory
roles
Handling
bureaucracy
“…when you’re a supervisor 1) you have a more regular
schedule and 2) you have a chance to influence the culture of the
branch, and that's really important to me.” (P14)
“Supporting the hands on people is definitely my favorite part of
the job but it also has the highest pressure because it’s usually the
tightest schedule and long hours but it’s also the most
rewarding.” (P25)
(8) Individual –
Continuous –
Indirect: “The
Bridgers”
Finding
opportunities
Getting
exposure to
technology
“Certainly serving on these [X] review panels gave me an
exposure to…connections to scientists and hearing about what
they are doing. I wouldn’t say there are a lot of competing
technologies but just maybe different applications. So I try to
listen to the needs and say, ok, you could use this.” (P16)
“[W]ithin research and development the people at the centers
have an advantage over the people at headquarters because they
have the ability to walk the halls and get their hands dirty, which
is so important in really understanding how research is
progressing… for that reason I admire the center level positions
because of their groundedness.” (P19)
5.2.1.1 The intrapreneurs
Intrapreneurs are technical professionals whose predominant motives are individual,
discrete, and direct motives. They are always in search of the resources that will allow
them to work on their own projects. They push their technologies forward by playing an
active role in marketing them. Although looking for resources and marketing technology
are not what they enjoy doing the most (these are relational and indirect activities by
nature), they do it because they know these activities are necessary for them to be able to
reach their goals. An intrapreneur would talk about his/her job in the following terms: “If
I don’t push this technology forward, no one will. I put a lot of effort into developing this
myself so now I won’t stop until I see it fly.”
60
5.2.1.2 The researchers
Researchers are technical professionals who are motivated by individual, continuous,
and direct motives. They are technically creative and enthusiastic about their work.
Researchers seek independence to be able to work on their own projects. They usually
and voluntarily work beyond the required work hours. Although they are similar to
intrapreneurs in terms of their passion for technical work and individual orientation, they
would rather constantly seek out new challenges than stick with one technology all the
way from conception to application. Their satisfaction comes from thinking of new ways
of doing things, doing them, and then taking on a new challenge. The enjoyment is
stronger during the search process than at the finding of the solution. A typical researcher
would talk about his/her job in the following terms: “Technically challenging work is
what motivates me the most. It would be ideal if I could spend all my time in the lab,
trying to solve problems without anyone bothering me.”
5.2.1.3 The enablers
Enablers are professionals who are motivated by relational, discrete, and indirect
motives. They have a strong commitment to organizational success and they feel
compelled to make it happen. Enablers usually seek supervisory roles where they can
have a broad view on the technical work that is being done in their organization.
Although they love technology and appreciate the detail work that goes into developing
and producing technology, they prefer to take supporting roles in the system such as
facilitating this work and monitoring progress. Although they do not enjoy handling
bureaucracy they choose to do it in order to enable the work of people doing direct,
technical work. A typical enabler would talk about his/her job in the following terms:
61
“You have to keep everything moving on a project, and make sure that our technical
people have what they need to get things done.”
5.2.1.4 The bridgers
Bridgers are professionals who are motivated by relational, continuous, and indirect
motives. They are always looking for ways to get exposure to technology and learn what
people are working on so that they can find opportunities to connect people with
technologies, and people with people. Bridgers play a key role in building networks
around those opportunities without getting too involved in the technical details of the
work or in the administrative aspects of it. They are interested in being in a role that
constantly facilitates the process of formal and informal connections both within and
outside the organization. This continuous endeavor is what differentiates them from
enablers, who focus on reaching specific goals using formal structures and measurable
outcomes. Bridgers are open-minded, visionary and maintain a broad view of
possibilities. An illustrative quotation of a bridger would be the following: “I like to
facilitate connections between our scientists and engineers. I feel proud to be in a role
that helps advance science and technology even when I’m not directly doing technical
work anymore.”
5.3 Discussion
In the first part of this chapter, we presented a model of scientists’ and engineers’
work motives that explains their different motivational orientations. These results are
theoretically generalizable to similar contexts. In other words, we expect to find the same
motives and relationships in comparable contexts. However, what we cannot predict is
the number or distribution of identities that we would expect to find in a similar context.
62
The study and characterizations of the complete set of identities is out of the scope of this
research and should be investigated in future research.
The discussion that follows is structured in two parts. First, we test whether our
identities explain the scientist versus engineer dichotomy at a finer resolution, or whether
they represent a broader spectrum of identities. Second, we discuss how our framing can
be used as a basis for improving incentive alignment in scientific R&D organizations.
5.3.1 Identities in the Traditional Dichotomy
In order to assess the traditional scientist versus engineer dichotomy, we compare
three commonly used classification schemes to our inductive model of identities. The
schemes involve classification by the employee’s (1) area of education (science or
engineering), (2) research degree (Doctoral or lower), or (3) job category (Science or
Engineering division). Figure 5-2 illustrates how our identity-based categorization fits
within the three approaches, represented by the three graphs within the figure. While
there is insufficient data to do a formal statistical test, the qualitative trend is apparent.
There is a weak correlation between categories of engineer and scientists and our
identity-based categories. To support the comparison, we mapped our identities to the
coarser categories of scientist and engineers as follows: enablers and bridgers were coded
as engineers (shades of red), while researchers and intrapreneurs were coded as scientists
(shades of blue). This grouping is based on the attributes that the literature normally uses
to describe scientists and engineers. For instance, the literature identifies management as
one of the career paths that engineers seek. In our categorization, enablers are
management-oriented identities; consequently, they would be classified as engineers.
63
Figure 5-2: Miscategorization of identities
Figure 5-2 shows the level of miscategorization of identities when using common
schemes. First, when separated by educational background -assuming that the area of
study reflects motivation- 40% of the engineers and 21% of the scientists by education
are miscategorized. Second, when separated by research degree – presuming that a PhD
implies a science orientation – the miscategorization error is 17% in scientists. And third,
when separated by job category (the most likely way they would be tracked in their
organization), there is a 57% error in engineers. Thus, separating by educational field
miscategorizes both scientists and engineers. Conversely, separating by research degree
only correctly categorizes engineers. This result could indicate that engineers, as broadly
defined by the literature, have little interest in pursuing doctoral degrees. Under this
logic, engineers who do pursue a doctoral degree, which is increasingly more common
nowadays, should be treated like scientists. Similarly, separating by job category only
correctly categorizes scientists. This result may indicate that scientists in the science side
of R&D organizations attract people with similar motivations. Also, this last result points
0%
25%
50%
75%
100%
Non-PhDs PhDs
Research orientation
(PhD or non PhD)
Enabler Bridger Researcher Intrapreneur
0%
25%
50%
75%
100%
Engineers Scientists
Education field
(science or engineering)
0%
25%
50%
75%
100%
Eng. Division Sci. Division
Structural separation
(engineering or science division)
40%
21% 17%
57%
64
out the need for a better understanding of the complex gray area where science and
engineering coexist.
The analysis in this section suggests that (1) our proposed scheme is not just a
refinement of the existing dichotomy but a broader set of identities, and (2) a large
number of individuals are currently being miscategorized, and as a result, not receiving
the incentives that will best motivate them.
5.3.2 Understanding and Using Incentives based on Identities
Incentive systems are based on assumptions about how employees will respond to a
particular inducement. The link between inducement and response can be explained in
terms of motivations. Incentives that are designed based on inappropriate assumptions
about underlying motivations will misfire. As we showed in the previous section, a large
number of individuals are currently being miscategorized and therefore they are not being
incentivized properly. This section describes how our findings can lead to more targeted
incentives.
5.3.2.1 Common incentives are not necessarily motivational
Recognition, money, promotions, and time-off are all desirable perks that
organizations commonly use to incentivize their workforce. By definition, incentives are
good things to have. Hardly anyone is going to decline recognition or an award.
However, the fact that they are “nice-to-haves” does not imply that people will change
their behavior to actively seek them. People value different things depending on the
motives that drive their behavior. As we show in our findings section, technical
professionals are motivated by social orientation, temporality of reward, and involvement
with technology motives.
65
Enablers typically seek supervisory roles. When becoming managers, they are the
ones who administer awards. It is reasonable that they favor incentives that make sense
and are exciting to them. And because these incentives are exciting to them, they will
most likely only affect like-minded employees. Enablers are relational and discrete
oriented. It is no surprise that most incentives emphasize those kinds of motives.
Based on our motive-based framework, we will discuss why common forms of
incentives misfire and how they can be transformed to increase their attractiveness to
people with different professional motivations.
Recognition: “There are these big awards that everybody likes”, claims enabler P24.
Recognition is indeed pleasant. However its impact on some people is marginal. For
example, P8 (researcher) in the quotation below states that recognition is less meaningful
compared to publishing a paper.
“[I]t's nice to have it [an award]. I don't do certain jobs because of that. I think of myself
as a research R&D person so to me… doing good work and have it published is more
gratifying than getting a piece of paper saying you did something great…” (P8)
Beyond the natural differences among people’s preferences, we can understand this
discrepancy between enabler and researcher by using our proposed framework of
motivation. It makes sense and it is exciting for enablers to give and receive recognition
awards 10 because they are dominated by relational and discrete motives. Getting
recognition from others and giving it to others in a ceremony brings up relational
motives. Recognition usually follows some specific accomplishment (such as high
10 “[The supervisory award…] is the most meaningful one for me because somebody who worked for me
had to sit down and write a nomination. That meant a lot to me.” (P24)
“I love being able to submit someone for an award and have them win it or assign somebody to a lead
position and have them really enjoyed and grow it. That is just really satisfying to me and I love it.” (P14)
“…it was very inspiring [to get an award] and when I got it I was very honored...” (P25)
66
performance, a discovery, etc.), which highlights discrete motives. And the whole idea of
attending an award ceremony also excites them (indirect activities). 11 Giving and
receiving awards is a discrete act of social acknowledgment. Under this logic, people
who are individual and continuous oriented (ex: researchers and intrapreneurs) do not
treasure these awards as much.
One way to make awards more impactful to some individuals is to emphasize the
third dimension: the involvement with technology. People who are direct oriented may
appreciate awards more when the focus is on the technical aspects of their
accomplishments. As the bridger P19 expresses in the quotation below, technical
employees appreciate awards that directly benefit their work over the ones that only give
them individual recognition.
“I would recommend awarding more access to your peer network. [Also], people love to
go to conferences, for example, it’s a pretty low cost award to get someone to say: “I
really appreciate the work that you’ve done, I want you to publish your results and I’m
going to send you to a conference and you can present that to your peers”. […] They’re
trying to establish themselves as an authority in their fields of expertise and through a
conference and through publications, that’s a direct line to doing that.” (P19)
Recognition awards are important incentive tools for organizations and must exist to
foster a sense of acknowledgement and pride. Recognition awards are also symbols that
represent what organizations deem as desirable behavior and outcomes. We argue that to
make incentives more impactful to employees it is important to understand their
dominant motives. For example, for researchers, who are individual, continuous, and
direct oriented, an award that emphasizes the effort (not just outcome) of their direct,
11 “I go to the awards ceremonies and see them dressed up. Sometimes there is a little luncheon or
something […] [B]eing able to give that to somebody [awards], being able to repay them for the hard work
that they’ve done, it just makes me feel so great.” (P14)
67
technical work is something that is potentially more stimulating than purely peer
recognition in social gatherings (award ceremonies).
Monetary awards: Recognition awards are usually accompanied by other incentives
such as money or time-off to make it more important. This, in theory and for many
managers, makes sense and sounds desirable. However, many times these additional
benefits have very little value for the employees. As the researchers P10 and P1 express
in the quotations below, the real reward is the work itself (direct).
“[Awards] are beneficial in fostering a feeling of recognition. If it does come with a real
tangible award, that feels more significant than just a plaque. [But] Is that spurring a lot
more innovation? I am interested in the work. I want to see the things succeed. Even if
you don't reward me I will still be interested in [technology] to work, to fly.” (P10)
“There is this yearly award, they have a few different categories…But, you know? People
who do this kind of work, they do it for the love of the job.” (P1)
Although recognition reinforced with monetary award does increase its value as an
incentive, it still lacks power. We suggest using measures that accentuate the things that
certain identities value. For example, for researchers and intrapreneurs, improving lab
equipment to work on the projects that they are most excited about would increase the
direct motivation. Another way to increase their direct and continuous motivation is to
give them monetary awards to visit other labs, learn something new, or go to a
conference.
Time-off awards: Another common award that usually complements recognition is
time-off. Most people like to have some time-off accumulated for when they need it. This
is a good thing to have in terms of job security, not a motivational incentive for work.
Actually, as we showed in the characterization of identities, some people, especially
68
researchers, tend to use their own time to work on what they like.12 So for them, time-off
awards do not mean much.
Although individual and direct motivated people greatly appreciate time to think
about the technical problems that they are interested in solving, they will probably not
take time-off because they do not want to miss out on the exciting things that go on at
work.13 And if they do, they will work anyway. Even for people who have gone back to
school, they would not leave their jobs because they are so interested in it that they prefer
to assume a higher work load (school and work)14 than to be away from work. We
suggest the creation of an alternative (complementary) time-off award: a time-off award
for internal use. Allowing researchers and intrapreneurs to use a percentage of their time
to work on their own ideas (emphasis on individual and direct motives) would be more
motivational than giving them time they do not need and/or will not use. Although
researchers and intrapreneurs already use their own time to work on what they want,
acknowledging and facilitating this practice would be appreciated and highly
motivational. Bridgers would also appreciate time-off for internal use to be able to
interact with people and make connections.
12 “I would prefer the cash to the time-off because I say I'm already voluntarily really working overtime.”
(P10)
“I spend a lot more hours than I get paid for doing my work but I kind of do that willingly, especially for
my research. I do that willingly because I’m interested and excited about the work.” (P7)
“I’ve gotten time off awards before and I come to work anyway, I don’t care... […] I use vacations but I
don’t stop working on what I’m working on. You just can’t do it. I can’t imagine doing it. It’s my life. It’s
what makes me tick.” (P17) 13 “They’ll say: “oh, you did all of this interesting work, then take the time off” and all the more interesting
work will pile up when you come back.” (P10)
“[M]ost of us don't go on vacations because we don't want to take the time away from work to do it.” (P1) 14 “I could have easily just gone back to school full-time for my PhD, which is probably the wiser thing to
do because I’m having two full-time jobs. But I love working here so I felt like I would be missing out on a
lot of really neat things if I did that so I wanted to try to get the best of both worlds.” (P7)
“There's no way I would've quit work and gone back to school full-time.” (P1)
69
Promotions: “Promotions are always a good motivator. People always like to be
promoted.” (P14). “The biggest [award] is that you get promoted, that is, up to your full
performance level.” (P20). P14 and P20 are both enablers that occupy management
positions. They believe that promotions are important motivators.
Enablers are relational, discrete and indirectly motivated. For them, the promotion
from purely technical roles into management roles has been coherent with their dominant
motives. In contrast, technical professionals who are driven by individual and direct
motives, such as P15 and P1 in the quotations below, express little interest in promotions.
“I’ve gone years and years and years without any promotion at all and hopefully one will
come through soon but clearly that’s not the motivator.” (P15)
“Those are kind of the two ways to get promoted: going into the line management and
project management and I don’t really want to do either one so for me, because most of
the rewards are from the work I just rather do the work.” (P1)
To be attractive, promotions paths need to reflect what people value. For example, in
the quotation below P1 (a researcher who agreed to assume a position in technical
management because he was the best candidate to do so) expresses how his current role
does not mirror his interests.
“I didn't really want the job to begin with because I knew that it would be a big headache.
I really like doing R&D… if I had a choice I'd much rather be in the lab than in a
meeting… I'm in meetings probably half my day and mostly my job is to find out what
needs to get done and then have somebody else do it. And then, I end up doing a lot of
paperwork…but it's not the kind of work that I would really like to do” (P1)
Another example is P22, a researcher in a managerial role who talks about
satisfaction in his job in the following terms:
“[I]f it was fun it wouldn't be called work […] I wouldn’t say that I come to work saying:
“oh, I look forward to this”, it’s more like: “I think I can get all my work done today” so I
would feel good about getting all my work done today.” (P22)
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To P22, getting things done is not really exciting or motivating but for the specific
work he is doing, it is the only source of satisfaction. And when asked about his future
career plans, management is not in the picture.
The existence or not of a clear promotion path does not seem to be a priority issue to
people with individual and direct motivation. As long as they can work on what they
want, promotion is not what is most motivational.15
As we have shown in this section, the common incentives are perceived differently by
different identities. By tweaking incentives to make them more attractive to populations
driven by certain motives, it is possible to make them more effective.
5.3.2.2 Alternative incentives can be motivational
Social activities: Some enablers in management positions mentioned how much they
enjoyed social activities. 16 For people who have relational motives for work, social
activities are desirable and probably effective in fostering a sense of belonging and group
cohesiveness. However, for individual oriented people, social activities may actually be
undesirable. One way to transform social activities into motivational instances for people
with individual orientation is to allow and encourage flexibility for informal work
meetings. 17 Although individual oriented people prefer to work in isolation, they
acknowledge the usefulness of interaction for work purposes, such as discussing ideas on
15 “If [my branch head] left or got promoted I would rather work in the branch with a good branch head
than be the branch head.” (P1) 16 “One of the things that we do every year in my branch, it isn’t necessarily an award thing but every year
we get together with the whole branch and have a picnic, we spend the whole afternoon out there. So that is
nice.” (P24) 17 “We always go to [X restaurant] on Fridays and that is our thing. That is our group thing. That is more
productive than a formal group meeting where you get in a lot of details and crap. Having the time to sit
and laugh with people and talk about concepts that someone may have or ideas that are crazy, that is where
those kinds of meetings happen, over informal time.” (P17)
“[What would make my job more enjoyable would be to have] meetings over coffee or just walking for
lunch. […] other people think: “oh, I just want to forget about my work during lunch, I don’t want to talk
about work.””(P3)
71
how to solve technical problems (direct), or learning from others so that they can use that
in their own work (continuous).
Technical conferences as incentives: Conferences are motivational to different
identities for different reasons. Relational and indirect oriented people view conferences
as instances of interaction, where their job is mainly to network (indirect work), which is
something that they enjoy. On the other hand, individual and direct oriented people are
the ones who usually present at those conferences. For them, conferences are also good
instances for interaction but what they enjoy is not the interaction per se, but the impacts
that the sharing of ideas can have on their own direct work. People are resources of
knowledge that can affect the direct, technical work so conferences are viewed as
beneficial to the work, not necessarily enjoyable. Generally, individual and direct
oriented people do not love the idea of attending conferences because it takes time away
from their own work and because going to conferences usually means more work
(prepare material, present, interact and on top of that, continue working on their own
work). 18 In spite of conferences not being their preferred activities, researchers and
intrapreneurs see a lot of benefit in attending conferences because it is a work-related
type of interaction that will benefit their work directly. Thus, using conferences as an
incentive makes sense to people and it is motivational.
The importance of conferences became evident a few years ago when the U.S.
government increased regulations and severely decreased the budget for conference travel
to all its agencies. This measure left a big impact on the technical staff at NASA, not
18 “We go to the conferences all day but then we are answering emails at night and doing all that stuff
because we can't really leave our work behind. But I think it's still fun, it's fun because it's actually
simulating. It gives you a chance to step away from the work in a different way.” (P1)
72
because they liked the trip but because they lost important interaction with their technical
and scientific community.19
In summary, a first step towards creating better incentive systems for technical
professionals in the R&D context is to understand preferences for incentives based on the
underlying motives that they promote. Expanding the scientist versus engineer dichotomy
to a broader set of identities can help tailor incentives better and therefore make them
more impactful.
19 “[There is a] perception of government wasting at conferences but conferences are the best places for
people to go and exchange knowledge with other people in the field and that’s how the field moves along.
If you don’t go and present your work to your peers, your work is not peer-reviewed, how do you know
that you’re doing well? How do you learn from other people that are working in the field? […] I work in
research. I work putting up new spacecraft. This is really cutting-edge stuff so you go to a conference to do
your job better for the future as well as to document the work that you have already done so that other
people don’t have to reinvent the wheel. I certainly think that conferences are crucial and essential and it
should be part of the job description...” (P7)
73
Scientists and engineers are essential to R&D organizations. Managing intellectual
human capital is challenging and critical to organizational success. The literature that
deals with the management of S&Es relies heavily on theories built decades ago that do
not reflect current patterns. While the context and characteristics of this workforce have
changed over the years, deeply embedded assumptions and broad generalizations about
S&Es have remained the same in the literature. In this dissertation, we identified the need
to revisit the underlying assumptions about technical professionals through deep
empirical work. There is a need to keep R&D management connected to the reality of
today’s workforce. From a practical perspective, managers need to understand their
employees’ motivations to be able to properly incentivize them.
In this research we tested the most important assumptions in the literature about
scientists’ and engineers’ motivations. Our results showed that the factors that are
believed to influence job satisfaction in scientists and engineers are indeed all important.
However, we did not find evidence of meaningful differences between scientists’ and
engineers’ preferences for those factors on job satisfaction. We also found that results
were highly sensitive to the way we define S&Es. Thus, a broad scientist versus engineer
dichotomy is not a useful way of representing motivations in this technical workforce.
Moreover, the measures that are commonly used to assess motivations on scientists and
engineers negatively limit our understanding of S&Es.
To overcome this limitation, we conducted a qualitative analysis to explore the
variety of motivations in technical professionals. Again, we found no strong support for
74
clear-cut distinctions between scientists and engineers. More specifically, we found three
dimensions of motives among technical professionals: the social orientation, temporality
of reward, and involvement with technology, with two extremes (categories) each.
According to the particular combinations of dominant categories and common behaviors,
we found four identities in our sample: enablers, bridgers, researchers and intrapreneurs.
These identities do not add granularity to the classical scientist versus engineer
dichotomy but offer a richer description of its middle ground. Particularly for incentive
design, we showed that existing categorization systems of professionals suggest
incentives that often do not align with their actual motivations. As a result, managers
miscategorize employees in the middle ground. While our study was not designed to
provide a prescriptive guide for incentive design, it does provide key factors for
consideration. More specifically, the dimensions identified in this study can be used to
evaluate how well current incentive systems are covering the spectrum of identities that
are possible to found in technical organizations.
6.1 Contributions
Our research contributes both theoretically and practically to the understanding of
motivational identities of technical professionals beyond a simplified dichotomy of
engineers versus scientists.
In terms of theoretical contributions of our first study, we agree with the literature in
that salary, level of independence, opportunities for advancement, and intellectual
challenge are important aspects that affect job satisfaction of scientists and engineers.
However, we find that these motivators are not enough to represent a scientist versus
engineer dichotomy. Technical professionals are motivated by additional factors that are
75
not frequently discussed in the literature (Petronio & Colacino, 2008). Since commonly
used measures of motivation and job satisfaction have limited explanatory power, our
results in the first study call for a deeper theoretical understanding in this area.
In the second part of our research we conducted exploratory research, which
contributed new theory on the motivations of scientists and engineers in the R&D
context. In particular, we found three areas of motivation that form a strong basis for
understanding the diversity of orientations in technical professionals as well as their
responses to incentives. The social dimension, temporality of reward, and involvement
with technology are three areas with two poles each that form a space for theoretical
comparison among professionals with different orientations.
In terms of practical contributions our results suggest that managers, in the absence of
better measures and using common measures of job satisfaction, should pay attention to
all four of these motivators: salary, advancement, independence, and challenge. Instead
of assuming an intrinsic tradeoff (ex. scientists care little about money and a lot about
independence), managers should strive to keep their technical professionals satisfied in
all of these areas, or at least as many as they can. If they are able to assess their
employees’ motivational identities, managers should use our proposed dimensions as
lenses with which to view both motivation and incentives. For example, if most
employees are inclined towards the relational side of the social dimension, then
incentives should be adjusted to emphasize that area. It is important to recognize how
incentives can trigger action – or no action – in each type of identity so that managers can
design stimuli that motivate employees in ways that are relevant to their objectives.
76
In conclusion, the knowledge derived from our study can be used to formulate
meaningful questions and create better measures of motivation, enrich behavioral
theories, and provide a basis for incentive design in technology-driven organizations. The
concepts presented in this work are potentially valuable in areas of organizational
analysis, workforce distribution, team building, and leadership, among others.
6.2 Limitations
In Phase 1 of this work we used secondary data, which poses some unavoidable
limitations to our research. First, our variables of interest (motives) are not directly
measured in the survey. Specifically, we use job satisfaction measures as proxies to
assess motivational preferences. Although these are related concepts that have been used
in the literature in the same way as they were used in this study, they are not the same.
Typically, job satisfaction is measured as a result of the presence of motivational factors.
This approximation of concepts may become a threat external validity. To avoid this
limitation, future research should develop more precise and direct measures of
motivation. Another limitation is that our model does not include variables that are
commonly related to overall job satisfaction such as satisfaction with supervisors, task
complexity, and other environmental factors. However, as we have said before, our
model is not meant to completely explain job satisfaction but to explain motivational
preferences based on the relative influence of a specific set of motivators. Also, when
choosing an aggregated and publicly available dataset, we sacrified precision of measures
for range and availability of the data. While our dataset covers a large range of
individuals, specifically people in science and engineering occupations in the U.S. as of
77
October, 2010, our results are generalizable to this particular population and may not be
transferable to other countries, professions, or generations.
I n Phase 2 of this research we used data from a relatively small sample that was
mainly composed of white, western, male interviewees. Although our sample size and
characteristics were not predetermined to be composed as such, we must acknowledge
that it could be biased to some extent and that a more diverse group of interviewees could
have yielded somewhat different results. This limitation is common to all studies that use
grounded theory method because the sample size is meant to ensure theoretical saturation
not statistical representativeness. Another limitation in qualitative research with human
subjects is that data is subjective and self-reported. This means that there could be some
differences in the quality of responses/interpretations across interviewees/researchers due
to their different abilities to look introspectively at their preferences/interpret results. To
overcome these limitations we asked questions in different ways when there was a doubt
about the quality of a response or an interpretation, and we compared and discussed the
coding of the interviews among a group of researchers.
Despite the limitations described in the previous paragraphs, we think that they
neither represent serious threats to validity nor prevent us from enriching our
understanding and advancing knowledge in the area of technical professionals’
motivation.
6.3 Future research
The following are some interesting pathways for future research that result directly
from the present research:
78
Building a tool to (1) assess technical professionals’ motivations based on the
dimensions found in this research, and (2) test their preferences and trade-offs
with respect to incentives. Motivation and preferences could be measured in a
survey. Since motivation is a latent variable, that is, a variable that cannot be
measured directly, it is fundamental to go through a process of construct
validation using pilot surveys before conducting the final survey. A valuable
alternative to measure preferences is to design a discrete choice model. Such
a tool could also be used to further study and characterize the behavior of all
possible identities in the model, especially the ones that we did not find in our
sample.
Discovering motive-based identity dynamics through the study of technical
professionals’ career progressions. This could be done in a cross-sectional
study using information from the resumes of professionals and a survey to
assess their identities. Alternatively, it could be done in a longitudinal study
where a cohort was followed over time and surveyed with respect to their
motivational and incentive preferences.
Testing how the distribution of motive-based identities in teams affects the
team’s performance. This could be done using a survey to assess the motive-
based identities of team members and compare team’s configurations in terms
of identities to relevant measures of performance. This knowledge could also
be useful in the hiring and training of new employees.
Understanding how generational changes (Baby boomers versus the Net
Generation), personality orientations, and diversity factors affect motivations
79
of technical professionals and consequently their preference for incentives in
the R&D environments. This could be investigated in a longitudinal study
with a diverse sample. Personality tests such as Myers-Briggs could also be
used to understand the potential relationships between motivational
preferences and personality types.
Expanding the understanding of the temporality of reward dimension.
Understanding how time perception affects behavior and performance results
among scientists and engineers is a potentially very powerful area for
incentive improvement. This kind of research would also require qualitative,
exploratory examination.
Replicating our qualitative study in a similar context could help refine the
theory that we have proposed in this research. Moreover, testing our theory in
different contexts would be an interesting way to understand its
generalizability beyond its limits.
80
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Appendices
Appendix A - Interview Guide
The following is the interview guide approved by the IRB in the early stages of this research project. Thus,
some explanations and questions in this guide may not be directly relevant to this research.
Understanding Technologists' Time Allocation and Career Progression in the Federal Laboratory
Context (IRB# 031337)
The first human subject portion of this study will use semi-structured interviews to collect data from
respondents. The goal of the interviews is to understand the scientists and engineers work activities and
preference structures for those activities in terms of time allocation decisions, at three NASA centers and
NASA HQ. Below is an outline of the types of questions that will be asked.
Background:
For how long have you worked at X? (experience)
Why did you choose NASA? (preference)
In which division/office did you start working when you first came to X, and what was your role?
How has that changed over time? (experience, career progression)
Work activities
How would you describe a normal workday for you? If there is no “normal workday”, what kinds
of days do you have? On what does that depend? What kinds of activities take most of your time?
(list of activities)
How different/complimentary are those activities? (characteristics of activities)
Which job activities do you like the most? Why? Which ones do you like the least? Why?
(preferences)
How much autonomy do you feel you have in your time allocation? If you had absolute freedom
to work on whatever you want, what would you do? (preferences)
Networks
How many people do you work with on a regular basis? Are they in your team? In a different
team? In a different organization? (network size)
Do they have the same expertise than you or do they specialize in different things? (network
diversity)
How important is for you to interact with them? (preference)
Incentives
In addition to your salary, how is your job currently rewarded? (incentives)
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How do you feel about it? Would you prefer to receive a different kind of recognition, award?
(preference)
What motivates you the most to work and what de-motivates you? (motivation)
Do you recall any incentive from the management side that has been attractive to you in the past?
(incentives)
Closing questions
How do you see your career in the future? Working on what, where, with whom? (expectations,
preferences)
Is there anything else you want to tell me?
Do you have any questions for me?
Thanks, if I have further questions, would it be ok if I contact you for a follow-up?
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Appendix B - Logistic regression complete results
All logistic regressions in this research were performed in STATA. The following image shows the results
of the logit regression with all our variables. The i. before each independent variable indicates that they are
dichotomous variables. The top part of the image shows how the log likelihood improved through several
iterations. Above the table and to the right, a summary of some regression measures can be seen. The last
one is the Pseudo R2, which is similar to the R2 used in linear regression. The table shows the variable’s
coefficients, standard error, z value, probability of error, and confidence intervals.
92
The following table displays a summary of goodness of fit measures for the previously shown regression.
93
The last table in this appendix also displays some of the measures that are used to understand the goodness
of fit in logistic regression. When classifying someone as very satisfied or less than very satisfied, our
model is right about 80% of the time.
94
Appendix C - Ranking of odds-ratios for combination of criteria
The following tables show the regression results in terms of odds-ratios using different combination of
categorization criteria given by the columns and far left rows in the tables. We also present the pseudo R2
for each regression.
Biologists Physicists
RANK R&D Non-R&D R&D Non-R&D
PhD
1 Salary (9.2) Advancement
(5.5)** Challenge (5.7)* Salary (6.6)
2 Challenge (5.2) Challenge (4.7)* ** Salary (5.2)* Independence (6.5)
3 Independence (3.8) Salary (4.1)* Independence (3.9) Challenge (5.1)
4 Advancement (3) Independence (3) Advancement (2.9) Advancement (2.6)
Pseudo
R2 0.42 0.41 0.3 0.37
Non-
PhD
1 Salary (4.9)** Independence
(22.4) Advancement (7.5) Salary (9.7)
2 Independence
(4.9)**
Advancement
(6.5)* Independence (6) Challenge (8.1)
3 Challenge (3.3)* Challenge (5.8)* Challenge (5.1)* Advancement (5.6)
4 Advancement
(3.3)* Salary (2.2) Salary (4.9)* Independence (2.5)
Pseudo
R2 0.31 0.46 0.4 0.45
Engineers Computer scientists
RANK R&D Non-R&D R&D Non-R&D
PhD
1 Salary (7.1) Salary (5.5) Challenge (6.1) Advancement (6.6)
2 Challenge (4.6) Advancement (4.4) Salary (4.5)* Independence (5)
3 Independence (3.8) Challenge (3.5) Independence
(4.4)* Salary (5)
4 Advancement (3.1) Independence (1.4) Salary (1.9) Challenge (4)
Pseudo
R2 0.41 0.28 0.38 0.41
Non
-
PhD
1 Salary (6.1) Salary (4.8) Salary (9) Salary (5.8)
2 Challenge (4.4) Challenge (3.4)* Independence (5.5) Advancement (4.7)
3 Independence (3.7) Advancement
(3.4)* Challenge (3.6) Challenge (3.2)
4 Advancement (3.5) Independence (2.8) Advancement (2.2) Independence (3)
Pseudo
R2 0.37 0.3 0.38 0.35
* Indicate overlapping confidence intervals for the odds-ratios.
** Indicate overlapping confidence intervals for the odds-ratios when there is a second overlap.
95
Appendix D - Predicted probabilities by work activity
The following table presents the predicted probabilities of high job satisfaction of biologists in all
possible combinations of work activity and satisfaction with the four aspects of the work that represent our
independent variables. These results are presented in Figure 4-2.
Delta-
method
Biologists Margin Std.Err. z P>z [95% Conf.Interval]
wa#jss#jsa#jsi#jsc
Basic_Res#Less#Less#Less#Less 0.054868 0.0010739 51.09 0 0.0527631 0.0569729
Basic_Res#Less#Less#Less#Very 0.1990636 0.003087 64.48 0 0.1930131 0.205114
Basic_Res#Less#Less#Very#Less 0.285196 0.0038555 73.97 0 0.2776394 0.2927527
Basic_Res#Less#Less#Very#Very 0.630743 0.0039102 161.31 0 0.6230793 0.6384068
Basic_Res#Less#Very#Less#Less 0.1761806 0.0037537 46.94 0 0.1688235 0.1835376
Basic_Res#Less#Very#Less#Very 0.4779631 0.0059185 80.76 0 0.4663631 0.4895631
Basic_Res#Less#Very#Very#Less 0.5951076 0.0058786 101.23 0 0.5835859 0.6066294
Basic_Res#Less#Very#Very#Very 0.8628728 0.0025574 337.4 0 0.8578603 0.8678853
Basic_Res#Very#Less#Less#Less 0.1853483 0.0031512 58.82 0 0.1791721 0.1915245
Basic_Res#Very#Less#Less#Very 0.4934288 0.0049152 100.39 0 0.4837952 0.5030624
Basic_Res#Very#Less#Very#Less 0.6099351 0.0048794 125 0 0.6003715 0.6194986
Basic_Res#Very#Less#Very#Very 0.870036 0.0021152 411.32 0 0.8658902 0.8741817
Basic_Res#Very#Very#Less#Less 0.4559715 0.0063493 71.81 0 0.4435272 0.4684159
Basic_Res#Very#Very#Less#Very 0.7820523 0.004012 194.93 0 0.7741888 0.7899157
Basic_Res#Very#Very#Very#Less 0.8520779 0.0031831 267.69 0 0.8458392 0.8583166
Basic_Res#Very#Very#Very#Very 0.9610306 0.0008498 1130.91 0 0.959365 0.9626961
App_Res#Less#Less#Less#Less 0.0420741 0.0006031 69.76 0 0.040892 0.0432562
App_Res#Less#Less#Less#Very 0.1582774 0.0020269 78.09 0 0.1543047 0.1622501
App_Res#Less#Less#Very#Less 0.2318712 0.002192 105.78 0 0.227575 0.2361674
App_Res#Less#Less#Very#Very 0.5637663 0.0028804 195.72 0 0.5581208 0.5694119
App_Res#Less#Very#Less#Less 0.1392677 0.0024272 57.38 0 0.1345104 0.1440249
App_Res#Less#Very#Less#Very 0.4092302 0.0046209 88.56 0 0.4001734 0.418287
App_Res#Less#Very#Very#Less 0.5265194 0.0045635 115.38 0 0.5175751 0.5354636
App_Res#Less#Very#Very#Very 0.8264128 0.002351 351.52 0 0.821805 0.8310207
App_Res#Very#Less#Less#Less 0.146857 0.0019251 76.29 0 0.143084 0.1506301
App_Res#Very#Less#Less#Very 0.4242794 0.0038526 110.13 0 0.4167285 0.4318303
App_Res#Very#Less#Very#Less 0.5419252 0.0036812 147.21 0 0.5347102 0.5491402
App_Res#Very#Less#Very#Very 0.8351166 0.001999 417.77 0 0.8311987 0.8390345
App_Res#Very#Very#Less#Less 0.3880503 0.0047598 81.53 0 0.3787212 0.3973794
App_Res#Very#Very#Less#Very 0.7308073 0.0037442 195.18 0 0.7234688 0.7381458
App_Res#Very#Very#Very#Less 0.8133685 0.0029713 273.74 0 0.8075447 0.8191922
App_Res#Very#Very#Very#Very 0.9491306 0.0008661 1095.89 0 0.9474331 0.9508281
Devel#Less#Less#Less#Less 0.057579 0.0010137 56.8 0 0.0555921 0.0595659
Devel#Less#Less#Less#Very 0.2073363 0.0033319 62.23 0 0.2008059 0.2138668
Devel#Less#Less#Very#Less 0.2957267 0.0034944 84.63 0 0.2888777 0.3025756
Devel#Less#Less#Very#Very 0.6425631 0.0041194 155.98 0 0.6344892 0.6506371
Devel#Less#Very#Less#Less 0.1837205 0.0034652 53.02 0 0.1769288 0.1905121
Devel#Less#Very#Less#Very 0.490725 0.0058273 84.21 0 0.4793037 0.5021464
Devel#Less#Very#Very#Less 0.6073584 0.0051366 118.24 0 0.5972908 0.617426
Devel#Less#Very#Very#Very 0.8688079 0.002423 358.56 0 0.8640588 0.8735569
Devel#Very#Less#Less#Less 0.1931886 0.0028597 67.56 0 0.1875836 0.1987935
Devel#Very#Less#Less#Very 0.5062032 0.0050228 100.78 0 0.4963588 0.5160477
Devel#Very#Less#Very#Less 0.6220221 0.0042412 146.66 0 0.6137095 0.6303347
Devel#Very#Less#Very#Very 0.8757056 0.0020958 417.85 0 0.871598 0.8798132
Devel#Very#Very#Less#Less 0.4686735 0.0055745 84.07 0 0.4577476 0.4795993
96
Devel#Very#Very#Less#Very 0.7906366 0.0037604 210.26 0 0.7832664 0.7980068
Devel#Very#Very#Very#Less 0.8584035 0.0026836 319.87 0 0.8531437 0.8636632
Devel#Very#Very#Very#Very 0.9628999 0.0007831 1229.54 0 0.961365 0.9644348
Design#Less#Less#Less#Less 0.0348596 0.0012651 27.55 0 0.0323801 0.0373392
Design#Less#Less#Less#Very 0.1339232 0.0044141 30.34 0 0.1252717 0.1425747
Design#Less#Less#Very#Less 0.1988685 0.0059043 33.68 0 0.1872963 0.2104408
Design#Less#Less#Very#Very 0.5152094 0.0092985 55.41 0 0.4969847 0.533434
Design#Less#Very#Less#Less 0.1174304 0.0039946 29.4 0 0.1096011 0.1252598
Design#Less#Very#Less#Very 0.3629104 0.0088577 40.97 0 0.3455496 0.3802712
Design#Less#Very#Very#Less 0.4776578 0.0094764 50.4 0 0.4590843 0.4962312
Design#Less#Very#Very#Very 0.7965402 0.0060702 131.22 0 0.7846428 0.8084375
Design#Very#Less#Less#Less 0.1240012 0.0040825 30.37 0 0.1159996 0.1320028
Design#Very#Less#Less#Very 0.3773443 0.0089531 42.15 0 0.3597964 0.3948921
Design#Very#Less#Very#Less 0.4931229 0.0094464 52.2 0 0.4746083 0.5116375
Design#Very#Less#Very#Very 0.8063909 0.0059368 135.83 0 0.794755 0.8180267
Design#Very#Very#Less#Less 0.3427371 0.0086186 39.77 0 0.3258448 0.3596293
Design#Very#Very#Less#Very 0.6906408 0.0081388 84.86 0 0.6746891 0.7065925
Design#Very#Very#Very#Less 0.7818436 0.0065553 119.27 0 0.7689955 0.7946917
Design#Very#Very#Very#Very 0.9388129 0.0021817 430.3 0 0.9345368 0.943089
C_apps#Less#Less#Less#Less 0.0843033 0.0014776 57.05 0 0.0814072 0.0871994
C_apps#Less#Less#Less#Very 0.2827165 0.0039071 72.36 0 0.2750587 0.2903743
C_apps#Less#Less#Very#Less 0.3875314 0.0043654 88.77 0 0.3789754 0.3960875
C_apps#Less#Less#Very#Very 0.7303771 0.0035298 206.92 0 0.7234589 0.7372953
C_apps#Less#Very#Less#Less 0.253258 0.0046913 53.98 0 0.2440632 0.2624527
C_apps#Less#Very#Less#Very 0.5921659 0.0057555 102.89 0 0.5808852 0.6034465
C_apps#Less#Very#Very#Less 0.6997803 0.0050804 137.74 0 0.6898229 0.7097377
C_apps#Less#Very#Very#Very 0.9089176 0.0018798 483.53 0 0.9052333 0.9126018
C_apps#Very#Less#Less#Less 0.2651457 0.0037986 69.8 0 0.2577006 0.2725908
C_apps#Very#Less#Less#Very 0.6070299 0.0047112 128.85 0 0.5977961 0.6162637
C_apps#Very#Less#Very#Less 0.7126256 0.0041379 172.22 0 0.7045155 0.7207356
C_apps#Very#Less#Very#Very 0.9139154 0.0015664 583.46 0 0.9108453 0.9169855
C_apps#Very#Very#Less#Less 0.5706638 0.0060446 94.41 0 0.5588166 0.5825109
C_apps#Very#Very#Less#Very 0.8505342 0.0030255 281.12 0 0.8446043 0.8564641
C_apps#Very#Very#Very#Less 0.9013329 0.0022399 402.39 0 0.8969427 0.9057231
C_apps#Very#Very#Very#Very 0.9750681 0.0005806 1679.53 0 0.9739302 0.976206
Mngnt#Less#Less#Less#Less 0.0955389 0.0012431 76.85 0 0.0931025 0.0979754
Mngnt#Less#Less#Less#Very 0.3114031 0.0036306 85.77 0 0.3042873 0.3185189
Mngnt#Less#Less#Very#Less 0.4206167 0.0031516 133.46 0 0.4144396 0.4267938
Mngnt#Less#Less#Very#Very 0.7565754 0.0027633 273.79 0 0.7511595 0.7619914
Mngnt#Less#Very#Less#Less 0.2801228 0.0041334 67.77 0 0.2720215 0.288224
Mngnt#Less#Very#Less#Very 0.6248966 0.0049592 126.01 0 0.6151767 0.6346166
Mngnt#Less#Very#Very#Less 0.7278438 0.0038511 188.99 0 0.7202957 0.7353919
Mngnt#Less#Very#Very#Very 0.9196756 0.0014469 635.61 0 0.9168397 0.9225115
Mngnt#Very#Less#Less#Less 0.2927771 0.0029801 98.25 0 0.2869363 0.2986179
Mngnt#Very#Less#Less#Very 0.6392944 0.0039403 162.24 0 0.6315715 0.6470173
Mngnt#Very#Less#Very#Less 0.7399346 0.0028782 257.08 0 0.7342934 0.7455758
Mngnt#Very#Less#Very#Very 0.9241324 0.0011872 778.41 0 0.9218055 0.9264593
Mngnt#Very#Very#Less#Less 0.6039661 0.0047834 126.26 0 0.5945908 0.6133414
Mngnt#Very#Very#Less#Very 0.8671804 0.0023955 362 0 0.8624854 0.8718755
Mngnt#Very#Very#Very#Less 0.9129009 0.001618 564.23 0 0.9097298 0.9160721
Mngnt#Very#Very#Very#Very 0.9782003 0.0004395 2225.49 0 0.9773388 0.9790618
97
The following table presents the predicted probabilities of high job satisfaction of engineers in all
possible combinations of work activity and satisfaction with the four aspects of the work that represent our
independent variables. These results are presented in Figure 4-1.
Delta-
method
Engineers Margin Std.Err. z P>z [95% Conf.Interval]
wa#jss#jsa#jsi#jsc
Basic_Res#Less#Less#Less#Less 0.0739793 0.0014398 51.38 0 0.0711574 0.0768011
Basic_Res#Less#Less#Less#Very 0.2134927 0.0035593 59.98 0 0.2065165 0.2204689
Basic_Res#Less#Less#Very#Less 0.1971665 0.0032969 59.8 0 0.1907047 0.2036283
Basic_Res#Less#Less#Very#Very 0.4548764 0.0051576 88.2 0 0.4447678 0.4649851
Basic_Res#Less#Very#Less#Less 0.1995917 0.0034902 57.19 0 0.192751 0.2064324
Basic_Res#Less#Very#Less#Very 0.4586606 0.005379 85.27 0 0.4481178 0.4692033
Basic_Res#Less#Very#Very#Less 0.4339298 0.005284 82.12 0 0.4235734 0.4442862
Basic_Res#Less#Very#Very#Very 0.7225762 0.0042332 170.69 0 0.7142792 0.7308732
Basic_Res#Very#Less#Less#Less 0.3094923 0.0045621 67.84 0 0.3005507 0.3184338
Basic_Res#Very#Less#Less#Very 0.6036311 0.0051637 116.9 0 0.5935105 0.6137517
Basic_Res#Very#Less#Very#Less 0.5794507 0.005178 111.91 0 0.5693019 0.5895994
Basic_Res#Very#Less#Very#Very 0.8239919 0.0030859 267.02 0 0.8179436 0.8300402
Basic_Res#Very#Very#Less#Less 0.5831625 0.0053732 108.53 0 0.5726311 0.5936938
Basic_Res#Very#Very#Less#Very 0.8261928 0.0031561 261.78 0 0.820007 0.8323785
Basic_Res#Very#Very#Very#Less 0.8113463 0.0033455 242.52 0 0.8047893 0.8179033
Basic_Res#Very#Very#Very#Very 0.9359498 0.0012904 725.32 0 0.9334206 0.9384789
App_Res#Less#Less#Less#Less 0.0948049 0.0007728 122.68 0 0.0932903 0.0963196
App_Res#Less#Less#Less#Very 0.2624609 0.0018236 143.92 0 0.2588866 0.2660351
App_Res#Less#Less#Very#Less 0.2435496 0.0016173 150.59 0 0.2403797 0.2467195
App_Res#Less#Less#Very#Very 0.5224339 0.0021737 240.34 0 0.5181735 0.5266943
App_Res#Less#Very#Less#Less 0.2463702 0.0020161 122.2 0 0.2424187 0.2503218
App_Res#Less#Very#Less#Very 0.5262375 0.0026143 201.29 0 0.5211135 0.5313614
App_Res#Less#Very#Very#Less 0.5012368 0.0025885 193.64 0 0.4961634 0.5063101
App_Res#Less#Very#Very#Very 0.7734787 0.0016662 464.22 0 0.770213 0.7767444
App_Res#Very#Less#Less#Less 0.3701177 0.0022071 167.7 0 0.365792 0.3744435
App_Res#Very#Less#Less#Very 0.6662784 0.0022202 300.1 0 0.6619269 0.6706298
App_Res#Very#Less#Very#Less 0.6436636 0.0021671 297.01 0 0.6394161 0.6479111
App_Res#Very#Less#Very#Very 0.8598944 0.0011463 750.16 0 0.8576477 0.8621411
App_Res#Very#Very#Less#Less 0.6471538 0.0025398 254.8 0 0.6421758 0.6521318
App_Res#Very#Very#Less#Very 0.8617217 0.0012953 665.25 0 0.8591828 0.8642605
App_Res#Very#Very#Very#Less 0.8493567 0.0013821 614.53 0 0.8466478 0.8520656
App_Res#Very#Very#Very#Very 0.9503898 0.0004769 1992.68 0 0.949455 0.9513246
Devel#Less#Less#Less#Less 0.071719 0.0005202 137.87 0 0.0706994 0.0727386
Devel#Less#Less#Less#Very 0.207927 0.0013912 149.46 0 0.2052004 0.2106536
Devel#Less#Less#Very#Less 0.1919226 0.0011769 163.08 0 0.189616 0.1942292
Devel#Less#Less#Very#Very 0.4465911 0.0018993 235.13 0 0.4428685 0.4503138
Devel#Less#Very#Less#Less 0.1942989 0.0015476 125.55 0 0.1912657 0.1973321
Devel#Less#Very#Less#Very 0.4503633 0.0023796 189.26 0 0.4456994 0.4550271
Devel#Less#Very#Very#Less 0.425728 0.002288 186.07 0 0.4212437 0.4302124
Devel#Less#Very#Very#Very 0.7158177 0.0017433 410.61 0 0.7124009 0.7192345
Devel#Very#Less#Less#Less 0.3023861 0.0017063 177.22 0 0.2990418 0.3057304
Devel#Very#Less#Less#Very 0.5955966 0.0021291 279.75 0 0.5914238 0.5997695
Devel#Very#Less#Very#Less 0.5712742 0.0019841 287.92 0 0.5673853 0.575163
Devel#Very#Less#Very#Very 0.8190855 0.0012355 662.95 0 0.8166639 0.821507
Devel#Very#Very#Less#Less 0.5750052 0.002436 236.04 0 0.5702307 0.5797797
Devel#Very#Very#Less#Very 0.8213344 0.0014413 569.87 0 0.8185096 0.8241592
Devel#Very#Very#Very#Less 0.8061703 0.0015061 535.28 0 0.8032185 0.8091221
Devel#Very#Very#Very#Very 0.933914 0.000556 1679.56 0 0.9328241 0.9350038
98
Design#Less#Less#Less#Less 0.0956613 0.0005248 182.28 0 0.0946326 0.0966899
Design#Less#Less#Less#Very 0.2643893 0.0013399 197.32 0 0.2617631 0.2670154
Design#Less#Less#Very#Less 0.2453853 0.0010779 227.65 0 0.2432726 0.2474979
Design#Less#Less#Very#Very 0.5249129 0.0014906 352.15 0 0.5219914 0.5278344
Design#Less#Very#Less#Less 0.2482202 0.0016248 152.77 0 0.2450356 0.2514048
Design#Less#Very#Less#Very 0.5287146 0.0020976 252.06 0 0.5246034 0.5328257
Design#Less#Very#Very#Less 0.5037213 0.0020343 247.61 0 0.4997341 0.5077086
Design#Less#Very#Very#Very 0.7752153 0.001262 614.27 0 0.7727418 0.7776888
Design#Very#Less#Less#Less 0.3724377 0.0015525 239.9 0 0.3693949 0.3754805
Design#Very#Less#Less#Very 0.6684845 0.001676 398.86 0 0.6651997 0.6717694
Design#Very#Less#Very#Less 0.6459399 0.0015321 421.6 0 0.642937 0.6489427
Design#Very#Less#Very#Very 0.8610875 0.0008384 1027.02 0 0.8594442 0.8627308
Design#Very#Very#Less#Less 0.6494199 0.0020415 318.1 0 0.6454185 0.6534212
Design#Very#Very#Less#Very 0.8629017 0.0010439 826.61 0 0.8608556 0.8649477
Design#Very#Very#Very#Less 0.8506239 0.0010966 775.68 0 0.8484746 0.8527733
Design#Very#Very#Very#Very 0.9508563 0.0003713 2561.12 0 0.9501287 0.951584
C_apps#Less#Less#Less#Less 0.0692572 0.0004465 155.12 0 0.0683821 0.0701323
C_apps#Less#Less#Less#Very 0.2018062 0.0012226 165.06 0 0.19941 0.2042025
C_apps#Less#Less#Very#Less 0.1861622 0.0009988 186.39 0 0.1842047 0.1881198
C_apps#Less#Less#Very#Very 0.4373238 0.0016373 267.1 0 0.4341147 0.4405328
C_apps#Less#Very#Less#Less 0.1884838 0.0014183 132.89 0 0.185704 0.1912637
C_apps#Less#Very#Less#Very 0.44108 0.002205 200.03 0 0.4367583 0.4454018
C_apps#Less#Very#Very#Less 0.4165678 0.0021018 198.2 0 0.4124484 0.4206871
C_apps#Less#Very#Very#Very 0.7081122 0.0016008 442.34 0 0.7049746 0.7112497
C_apps#Very#Less#Less#Less 0.2945187 0.0015048 195.71 0 0.2915692 0.2974681
C_apps#Very#Less#Less#Very 0.5865143 0.0019477 301.13 0 0.5826969 0.5903317
C_apps#Very#Less#Very#Less 0.5620472 0.0017684 317.83 0 0.5585813 0.5655132
C_apps#Very#Less#Very#Very 0.8134503 0.0011237 723.88 0 0.8112478 0.8156528
C_apps#Very#Very#Less#Less 0.5657975 0.002301 245.89 0 0.5612876 0.5703075
C_apps#Very#Very#Less#Very 0.8157535 0.0013788 591.62 0 0.813051 0.8184559
C_apps#Very#Very#Very#Less 0.8002309 0.0014317 558.94 0 0.7974248 0.803037
C_apps#Very#Very#Very#Very 0.9315566 0.0005251 1774.13 0 0.9305275 0.9325858
Mngnt#Less#Less#Less#Less 0.0979048 0.0005586 175.28 0 0.0968101 0.0989996
Mngnt#Less#Less#Less#Very 0.2694112 0.0014426 186.75 0 0.2665838 0.2722386
Mngnt#Less#Less#Very#Less 0.250169 0.0011617 215.35 0 0.2478922 0.2524458
Mngnt#Less#Less#Very#Very 0.5313092 0.0016377 324.42 0 0.5280993 0.5345191
Mngnt#Less#Very#Less#Less 0.2530407 0.0016536 153.02 0 0.2497996 0.2562817
Mngnt#Less#Very#Less#Very 0.535105 0.0021493 248.96 0 0.5308924 0.5393176
Mngnt#Less#Very#Very#Less 0.5101367 0.002064 247.16 0 0.5060913 0.5141821
Mngnt#Less#Very#Very#Very 0.7796562 0.0013028 598.47 0 0.7771029 0.7822096
Mngnt#Very#Less#Less#Less 0.3784561 0.0015837 238.96 0 0.375352 0.3815601
Mngnt#Very#Less#Less#Very 0.6741478 0.0017222 391.44 0 0.6707723 0.6775233
Mngnt#Very#Less#Very#Less 0.6517876 0.0015619 417.3 0 0.6487263 0.6548489
Mngnt#Very#Less#Very#Very 0.8641292 0.0008681 995.37 0 0.8624277 0.8658308
Mngnt#Very#Very#Less#Less 0.6552408 0.0020097 326.04 0 0.6513019 0.6591798
Mngnt#Very#Very#Less#Very 0.8659099 0.0010363 835.54 0 0.8638787 0.8679411
Mngnt#Very#Very#Very#Less 0.8538559 0.0010771 792.77 0 0.8517449 0.8559669
Mngnt#Very#Very#Very#Very 0.9520419 0.0003715 2562.8 0 0.9513138 0.95277
99
Appendix E - Predicted probabilities under different scenarios
To be able to interpret these graphs, please look at the variables in the title. The first variable is the on the
x-axis, and the following variables (separated by #) are the combination of characteristics in the curve.
These graphs are generated by the conditions indicated in the rows right above each graph.
Probabilities of being very satisfied with the job if satisfied with…
Independence and challenge Salary and advancement
Challenge and advancement Independence and advancement
JSI JSS
JSC JSI
100
Salary and independence Salary and challenge
Only salary All but salary
JSS
+JSI
JSS +JSC
JSS NOT JSS
105
Appendix F - Quotations examples by code (summary)
The following table offers quotation examples for each motive code presented in Figure 5-1.
Category Motive code Quotation example
Ind
ivid
ual
Doing own
work
“I like the idea that when I come in in the morning I can kind of, kind of,
pick what I want to work on that day so if I'm sick from the flight project
work I can go do some technology development for that day and then pick
up the flight project work later.” (P21)
Having
independence “I like having autonomy. I like being able to define what I work on.” (P15)
Work on what
I want
“it’s the freedom to work on what you like, that’s the best thing. Even
though sometimes you have to work on what your boss says to work on but
it’s more than 50% freedom to work on what you like and also freedom to
select the topic that you want to work on.” (P3)
Rel
atio
nal
Helping others “I like working with them I tried to help them develop ideas and then I try to
help them connect those ideas and those people with funding” (P24)
Working with
others
“I really liked working here, I like the vision of it, it’s a very collaborative
atmosphere” (P14)
Interacting
“working directly with other engineers to get stuff done but that is also
sometimes frustrating to but when you work well together it's worth it.”
(P11)
Dis
cret
e Delivering “My favorite is to see that we delivered things.” (P18)
Seeing things
fly
“one of the highlights of my career was that I was able to see the launch of
[the instrument he was working on] on the space shuttle” (P25)
Getting things
done
“you’ve got to be focused on getting the thing done and you doing it by
yourself is not the point of it.” (P5)
Co
nti
nu
ou
s
Contributing
to science
“The main driver for all these things is the science so we have to first make
sure that the science is sound, like why are we even doing this?” (P3)
Learning
“I always liked to learn so I wanted at least to get a Masters for the purpose
of learning and then when I was doing that…. There is an incentive that [the
center] is providing that they can a pay for it so I looked at that as an
opportunity. They were providing me the opportunity so I wanted to take
advantage of that.” (P7)
Understanding
phenomena
“I still like to think of myself as a scientist… I’ve tried to and still want to
understand how things work. […] From my perspective, engineers take
things that are already known and apply them to the problem and a scientist
tries to understand what is going on.” (P13)
Dir
ect
Doing hands-
on technical
work
“To me, the reward is just being able to go up and do the lab work” (P1)
Doing creative
work
“I like the creative aspects of my work: being able to think of new ways of
doing things.” (P9)
Working on
new technical
problems
“when I’m doing my research and I’m getting new data, I’m pushing the
envelope of what is known by other people that’s really neat. When I get
new data about something that nobody else has ever done before, that’s kind
of exciting.” (P12)
Ind
irec
t
Influencing
technical work
“supporting the hands-on people is definitely my favorite part of the job”
(P25)
Finding
opportunities
“I’ve had to spend more time looking more broadly across our branch as
well as our division and directorate and the center about what technology is
first and foremost needed by our community, by the broader space industry
as well.” (P19)
Facilitating
collaborations
“my favorite part of the job is enabling people to do really novel things and
facilitating their work and interactions.” (P4)
106
The table below presents additional quotation examples of the action codes displayed
in Table 5-1.
Identity Action codes Quotation example
Intr
apre
neu
rs
Marketing
own
technology
“On the good side of things [competing for funding] allows you to… well, it
forces you to think strategically and think through the thing that you think is
the best to work on and you have to sell it.” (P15)
Finding
resources for
own projects
“Everyone have their own problems to work on but you have to convince
people that is critical, that the agency has got to have it so it's not going to be
met, that we are going to look bad if we are not going to get this done.”
(P17)
Res
earc
her
s Seeking
independence
“you have to make your own… You have to take charge of your own destiny
I guess.” (P8)
Work all the
time
"The job is nice and flexible. If you need to slip out for two hours you just
work two hours some other time, I don’t end up using my vacations on a
yearly basis” (P12)
En
able
rs
Seeking
supervisory
roles
“…the only conscious choice to go into management would be to come to
this job” (P4)
Handling
bureaucracy
“…they tend to be very focused on what they’re interested in and pretty
much nothing else and the bureaucracy annoys them… they don’t want to
deal with it and so that makes my job more difficult because I have to deal
with it and often times I need them to help me deal with it and sometimes it’s
hard to get them interested to deal with the bureaucracy and just the simple
fact of writing a proposal in a way that it will get funded instead of the way
they want to write it, the way they think it should be written” (P24)
Bri
dg
ers
Finding
opportunities
“I see a lot of ideas. We do an exhaustive search -everything in the literature-
, contacts, [and] a lot of it is from conferences, networks, [and] people
visiting. Usually if somebody has a really great idea we will be out there
[calling their attention] and we will try to pick it up.” (P16)
Getting
exposure to
technology
“[By working there] I developed a pretty good understanding of that [his
area] as well as a pretty good understanding of the available technology and
what’s happening in the community: who is doing what and be able to bridge
and make those two gather.” (P9)