scientists, engineers, or both? motives and preferences of

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.



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

iii

© Copyright 2015 by Isabel Bignon

All rights reserved

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 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


dissatisfied


advancement jsa Dummy 1= very satisfied


dissatisfied


independence jsi Dummy 1= very satisfied


dissatisfied


challenge jsc Dummy 1= very satisfied


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:


(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:


+ 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-


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-


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)

70

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.

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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

101

All but independence All but advancement

All but challenge All

NOT NOT JSA

102

None Independence and challenge

Advancement and satisfaction

103

All Salary

All None

104

Not advancement Not independence

Not challenge Not salary

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)

scientists, engineers, or both? motives and preferences of

Documents