From Problem Solvers to Solution Seekers- The Permeation of Knowledge Boundaries at NASA

8/16/2019 From Problem Solvers to Solution Seekers- The Permeation of Knowledge Boundaries at NASA

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From Problem Solvers to Solution Seekers: The Permeation

of Knowledge Boundaries at NASA

Hila Lifshitz-Assaf

New York University

Leonard N. Stern School of Business New York, NY 10012

Tel: 212-998-0810

[email protected]

January 4, 2015

-Draft, Please do not distribute-

Acknowledgments: I thank the United States National Aeronautics and Space Administration (NASA),

especially NASA’s space life science directorate (SLSD) and its organizational members who shared their

daily work life, experiences and thoughts with me and welcomed me to their workshops, meetings and

labs. I thank my main adviser and mentor, Michael Lee Tushman and devoted committee members,

Michel Anteby and Karim R. Lakhani; as well as Leslie Perlow, Carliss Baldwin and Elizabeth Hansen

for extensive feedback on several versions of this paper, and participants in the inductive qualitative craft

group and WOM Seminar, for their enthusiastic support. I am also grateful for feedback from participants

at the organization science winter conference, the structure and structuring of work in organizations

workshop, the national science foundation knowledge conference, Wharton people and organization

conference, as well as seminars at Harvard University. This research was funded by Harvard Business

School’s Division of Research and Faculty Development.


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Abstract

Innovation scholars have long been modeling and arguing about the optimal way to organize for

the production of scientific and technological innovation. Until recently, the prevailing

consensus among various streams of theoretical and empirical literature has been that innovation

does and should take place within the boundaries of the firm. However, recently a new model,

usually named “open” or “peer production” innovation, is challenging the permeability itself of

these boundaries. According to this model, knowledge work should transcend the traditional

professional and knowledge boundaries, and instead be conducted in the “open”, by anyone who

chooses to contribute. This study is a longitudinal in depth field study of how R&D professionals

permeate their boundaries. In an open innovation experiment at NASA, R&D professionals

experimented with posting their strategic R&D challenges on open innovation online platforms

and communities. This experiment resulted in an important scientific breakthrough in

unprecedented speed. However, challenging the permeability of professionals’ knowledge

boundaries, posed a challenge on their professional identity. This paper describes the reactions of

these professionals and finds that professional identity refocusing work- the ability to change,

reconstruct and re-narrate one’s professional identity-is critical for permeating professional’s

knowledge boundaries and shifting the locus of innovation. I discuss the contributions and

implications of these findings on organizing for scientific and technological innovation,

knowledge boundaries and professional identity.

Keywords: innovation; knowledge boundaries; boundary work; professional identity; open

innovation; identity work; knowledge boundary work; technology, work and organizations


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“It is a deep philosophy change, from being a problem solver to a solution seeker...

it is not only about the organizations [we work in], this is how we have been trained

ever since we are kids- to solve problems! To be the experts of a field and solve!”

-Marie, an engineer, NASA, June 2012

Since Schumpeter (1934), social scientists have been modeling and arguing about the optimal

way to organize the production of scientific and technological innovation (see Baldwin and Von

Hippel, 2011). Until recently, the prevailing consensus among various streams of theoretical and

empirical literature has been that innovation does and should take place within the boundaries of

the firm. There is a well-established literature examining the organizational knowledge creation

processes related to innovation (Bechky, 2003a, 2003b; Carlile, 2002, 2004; Hargadon, 2003;

Kogut and Zander 1992, Dougherty, 1992; Nonaka, 1994; Nickerson and Zenger, 2004; Owen-

Smith and Powell, 2004; Orlikowsky, 2002; Tushman, 1977). However, recently, scholars are

arguing for a shift in the locus of knowledge creation and innovation outside the boundaries of

the traditional processes due to recent changes introduced by digital technologies (Baldwin and

von Hippel, 2011; Benkler, 2006). This approach is usually named “open”, “peer production” or

“distributed” innovation (Benkler, 2006; Chesbrough, 2003; Von Hippel, 1988, 2005) and is

inspired by the “open source” methods of organizing for innovation which have demonstrated

the possibility of innovating successfully outside of the traditional economic and organizational

boundaries (Benkler, 2006; Lerner & Schankerman, 2010; O’Mahoney and Ferraro, 2012;

O’Maoney and Lakhani, 2011; Raymond, 1991).

A growing number of professions and organizations are experimenting with such “open

innovation” models to various degrees, however we sorely lack related empirical research

(Chesbrough, Vanhaverbeke and West 2014; Helfat, 2006; Gann, 2005; Lakhani, Lifshitz-Assaf

and Tushman, 2013; Dahlander and Wallin, 2006; Henkel, 2006; West and O’Mahony 2005).


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Most of the research on such models focuses on the online platforms and communities

(Dushnitsky and Klueter, 2010; Fleming et al., 2007; Lerner and Tirole, 2002; West, 2003;

Boudreau et al., 2009; Jeppesen and Lakhani, 2009; Terwiesch and Xu, 2008) and not of the

organizations and professionals using them. As innovation and organizational theory scholars our

interest is peaked by the promise of this approach to produce knowledge and innovation, yet we

expect insurmountable obstacles. As social scholars we expect that such an opening of the

organizational innovation process would threaten several fundamental social structures in the

organization and meet with implacable resistance. As the phenomenon begins to spread, the

questions remain unanswered: What is the ensuing impact inside organizations? How does using

open innovation influence R&D professionals and their work? How do R&D organizational

members permeate these boundaries and shift the locus of innovation?

Prior literature on knowledge and innovation in organizations suggests that R&D

organizational members would fiercely reject opening their problems to be solved by external

members of online platforms and communities. The boundaries of knowledge and innovation

processes have been demonstrated to be an important and contested area for R&D organizational

members (Bechky, 2003a, 2003b; Brown and Duguid, 2000; Carlile, 2002, 2004; Dougherty,

1992; Orlikowsky, 2002; Szulanski, 1996). Knowledge is an embedded and invested practice

and therefore is “at stake” for those actors who have developed it (Carlile, 2002). Beyond the

notorious not-invented-here syndrome (Allen, 1977; Katz and Allen, 1982), there are unique

reasons to reject knowledge from open innovation platforms and communities since they attract

individuals on the margins of the knowledge boundaries (Jeppesen and Lakhani, 2010) and

therefore can pose a threat to the specialized knowledge developed within the organization.


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The sociological “Boundary work” and professionalism literature also predicts such a

rejection. The literature on professions has ample studies of professions collectively determining

criteria to keep "unqualified" members out of the profession, lay claim to a specific knowledge

base (Sarfatti-Larson, 1979) and establish boundaries to help distinguish laymen from experts

(Lamont and Molnar, 2002). Gieryn, who coined the “boundary work” construct (1983), stresses

that expansion, monopolization and protection of boundaries are at the heart of

professionalization. Solving challenging scientific and technological problems is often the main

motivator for entering the profession and surely it’s most prestigious activity. Therefore unlike

common “hiving off” practices (Hughes, 1958; Smith, 1987) in which professions allocate their

more routine duties to others, the open innovation model threatens to deprive the profession of its

most prestigious task. As Van Maanen and Barely explained “Technological innovations which

are interpreted as potentially deskilling or which might disrupt the social structure and prestige of

the community as it is currently organized will be resisted and, if possible, sabotaged” (1984:90).

In the same vein, according to Abbott’s theory of professionalism (1988), the main focus

of professions is to gain legitimacy over tasks and problems through protecting and expanding

their professional jurisdiction. As Abbott explains; “The history of professions is a biography of

the relationship between problems and the tasks that seek to resolve them” (1988: 285).

Professions build themselves through legitimizing the type of problems they can solve and

therefore are within the boundaries of their jurisdiction and therefore having other professionals

solve their problems is a clear threat. Opening these tasks and problems to essentially anyone

would put both professional jurisdiction and legitimacy at risk, contradicting the very

foundations of reigning theories. The new paradigm of the web however, is changing knowledge

work and the permeability of multiple boundaries, challenging professional jurisdictions and


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organizational boundaries in theoretically unexplored ways. Recently, there has been a call to re-

examine the literature and our theories regarding knowledge production and scientific work

(Janasoff, 2004; Leonardi and Bailey, 2008). In particular, scholars have been calling for an

exploration of the use of open innovation by organizations in greater detail (Gann, 2005; Helfat,

2006; Lakhani, Lifshitz-Assaf and Tushman, 2013; Murray and O’Mahoney, 2007), yet to date

there are few papers that have systematically undertaken this endeavor (Dahlander and Wallin,

2006; Henkel, 2006; Laursen and Salter, 2006, 2014; West and O’Mahony, 2005). This study is a

first step in filling this gap.

In order to investigate the shifting locus of knowledge and innovation production, I use

the concept of “knowledge boundary work”, capturing the actions behind the change to

knowledge boundaries. This concept is inspired by and borrowed from the studies of “boundary

work” in sociology of science and knowledge; leveraging it as “an immensely useful concept to

illuminate the social organization of scientific knowledge” (Gieryn, 1983, 1999; Lamont and

Molnar, 2002; Nelson and Lawrence, 2012). Investigating knowledge boundary work requires an

in depth investigation of the work itself of the R&D professionals (Anteby, 2008a; Barley and

Kunda, 2001;1994; Bechky, 2003b; Huising, 2015; Owen-Smith, 2001). Such a close

investigation enables examining the change in integration of external knowledge flows inside the

R&D process and openness and sharing of internal knowledge work following the use of open

innovation methods.

Most research about boundaries (organizational, professional, knowledge or symbolic)

and boundary work investigates the existence of boundaries and the challenges fraught with

working across such boundaries (e.g., Carlile, 2004; Dougherty, 1992; Lamont and Molnar,

2002; Levina and Orlikowski, 2009). The boundary spanning literature, for instance, assumes the


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permeability of these boundaries as a given and a constant construct (Levina and Vaast, 2005;

Obstfeld, 2005; Rosenkopf and Nerkar, 2001; Tushman, 1977). In contrast, this study sheds light

on a new type of boundary work that changes the actual formation and permeability of the

boundaries themselves, namely boundary permeation work . This advances our understanding of

the significant and underexplored changes in the nature of knowledge work within organizations

that the Internet has brought about (Argote, McEvily and Reagans, 2003; Levina and Vaast,

2008; Laursen and Foss, 2012; Murray and O’Mahoney, 2007). Moreover, the concept of

knowledge boundary work suggests a resolution to the fiercely debated novelty of the open

innovation phenomenon (Cassiman and Valentini, 2013; Trott and Hartman, 2009). The term

“open innovation” has become exceedingly unclear as it is used to describe such a wide range of

knowledge creation and innovation processes at organizations (Dahlander and Gann, 2010). It is

unclear which innovation processes qualify as “open” and, more critically, how this approach

differs from the traditional cross boundary innovation collaborations that have been extensively

studied (Gann, 2005; Helfat, 2006; Murray and O’Mahoney, 2007). The types of knowledge

boundary work explored by this study, and the resulting set of possible boundary types

contribute clarity to the topic and its fuzzy terms. It elucidates the multiple meanings of “open

innovation” as reported by organizations and their members in the popular literature by focusing

not only on the rhetoric but also on the knowledge activities of R&D organizational member as

they react to and enact “open innovation”.

Most studies of knowledge and innovation processes do not investigate the identity of the

members involved and its impact. The notable exceptions are studies that illustrate the effect of

pre-hoc organizational identity and technological framework on resistance to technological

change (Benner and Tripsas, 2012; Kaplan, Murray and Henderson, 2003; Kaplan and Tripsas,


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2008; Tripsas, 2009). This study builds on their work and expands it to the individual level in the

case of change. Challenging professional boundaries and jurisdictions creates tensions and

threatens professionals’ identity (Hornsey and Hogg, 2000; Mitchell et al., 2011). Prior studies

on identity threats (Elsbach, 2003; Huy, 2011) would clearly predict that R&D organizational

members reject opening their boundaries through online platforms and communities.

Contemporary identity literature is shifting from viewing identity as relatively enduring and

resilient to a more dynamic and nuanced view focused on the processes related to identity

construction and reconstruction (Gioia et al., 2000; Lepisto, Crosina and Pratt, 2013; Pratt,

2012). Identity scholars have developed the concept of “Identity work” to explain the “range of

activities that individuals engage in to create, present, and sustain personal identities that are

congruent with and supportive of the self-concept” (Snow and Anderson, 1987: 1348).

Investigating both knowledge boundary work and identity work of R&D organizational

members deepens our understanding of the overall relationship between "doing" and "being" of

professionals (Anteby, 2008b; Elsbach, 2009; Glynn, 2000; Kaplan and Tripsas, 2008; Pratt et

al., 2006; Tripsas and Gavetti, 2000). Identity work is often observed in narratives and other

symbolic and rhetorical strategies of organizational members (Alvesson, 1994; Corley and Gioia,

2004; Gioia, 1986; Ibarra and Barabulescu, 2010; Rafaeli and Pratt, 2006). Alternatively, other

scholars emphasize the role of "working and doing" in the creation of self (Anteby, 2008b;

Glynn, 2000; Van Maanen, 1997; Wrzesniewski and Dutton, 2001). This study investigates both

the change in organizational members’ innovation narratives (Bartel and Garud, 2009) and also

what they did in their R&D work. This is particularly important since “open innovation” can be

viewed as a managerial fad, an analysis of mere rhetoric can be misleading (Zbaracki, 1998).

Research Setting


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To study the influence of open innovation on R&D professionals and their work, I conducted an

in-depth longitudinal study at the US National Aeronautics and Space Administration, NASA,

more specifically at NASA’s space life science directorate (SLSD) and related units. The study

started in 2009 and ended in 2011, designed as an inductive study in one organization, enabling

in-depth analysis, similar to most studies of technology in organizations that follow one

organization throughout the process of introducing a new technology (Barley, 1986, 1990;

Orlikowsky, 2007, 2008; Zuboff, 1988). NASA was an appropriate fit as a field site since they

conducted a significant experiment with open innovation; they opened strategic R&D challenges

and therefore their R&D organizational members were heavily involved in the experimentation.

Furthermore, NASA has fewer intellectual property concerns which enabled focusing on the

organizational issues raised by open innovation. Focusing on the population of NASA’s R&D

organizational members as the unit of analysis and not the organizational level makes this field

study more representative since their population is similar to equivalents life science R&D

organizations. Lastly, investigating the experience of R&D organizational members within the

same organization enabled “controlling” the field and organizational environment and focusing

on the variation of the experience of open innovation.

Data Collection

This study ran for three years between 2009 - 2012, throughout this period I collected data and

conducted iterative cycles of analysis and collection, pre and post open innovation experiment. I

collected data on 98 organizational members across 28 organizational units relying on three main

sources: observations, interviews and internal surveys, documents and communication (see Table

1). My data collection was aimed at gaining a deep understanding of the day-to-day work

experience of organizational members at NASA as a necessary basis to understand their open


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innovation experience in context (Kunda, 1992; Van Mannen, 1988). Therefore in each of the

data sources detailed below, I started with collecting data on the day-to-day work and only over

time focused on the open innovation experiment and its impact.

Observation: In order to have a longitudinal view I collected data in the field for an average of 1

week every other month for 2 years and complemented the collection with remote participation

in workshops and meetings (NASA SLSD is physically located at Johnson Space center (JSC), in

Houston, Texas). First, to gain an in depth understanding of the day-to-day experience of

members in the organization, I observed and participated in various workshops, meetings and

training sessions. Then, to explore the influence of the open innovation experiment on their

work, I observed R&D meetings, demos of innovative projects and joined off- site meetings with

potential collaborators (such as General Electrics) and contractors (such as Wyle Laboratories). I

observed and recorded all the “open innovation” workshops and meetings and participated at the

first NASA workshop for collaborative innovation with the White House Office of Science and

Technology. Lastly, since the entire space industry was going through momentous change I

joined the organizational members to the annual “humans in space” conference and to the

astronauts debrief for family and friend (of the last shuttle flights). I asked permission to record

all my observations in meetings and workshops, and each day of observation yielded audio

recording of the meetings that were later transcribed and handwritten field notes that I wrote up

within 24 hours of leaving the field (resulting in approximately 1,000 pages of field notes).

Interviews: I conducted semi-structured interviews with R&D organizational members who

varied in their level of participation in the open innovation processes and in their professional

background (see Table 1). Overall, I conducted 104 interviews, 87 formal interviews with 70

individuals and 17 informal interviews with 12 individuals. The interviews were an average of an


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hour and a half long, producing about 25-60 pages of an interview transcript and notes (resulting

in approximately 500 pages of interviews transcripts and notes).

Project Work documents, Internal Surveys and Emails: Throughout the study, I received

access to related project work documents written and presented by organizational members. I

collected 94 project documents and 25 presentations (resulting in approximately 1,100 pages). In

addition, I collected the public publications and patents produced by these R&D professionals.

These work documents had a crucial role in understanding the change in the R&D work that took

place as a reaction to the open innovation experiment. It enabled me to analyze both the way the

external knowledge found through open innovation was integrated or not, and the degree to

which the internal knowledge production process was shared (see Table 2). I was provided

access to internal surveys regarding the experiment, as well as interview transcripts and

summaries with key organizational members. These internal sources of data enabled me to

triangulate and enrich my interview and observation data. Lastly, I subscribed to and collected

data from the internal email list pertaining to the open innovation initiatives (overall

approximately 110 internal emails).

Secondary Data Sources: The focus of this study is the organization and its members, however,

in order to have more comprehensive understanding of the open innovation experiment, I

collected not only data from NASA, but also data from the open innovation platforms:

Innocentive, Top Coder and Yet2.com. This data is both quantitative and qualitative in nature.

For instance, type and number of external solvers who tried solving it, professional background

and geographical location, proposed solutions, amount of awards, and more.

Data Analysis


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I conducted an iterative process of data collection and analysis according to the insights and

theoretical categories I induced from the field data, thus creating a theory that is “grounded” in

the data (Glaser and Strauss, 1967; Miles and Huberman, 1984). For the first two years of this

study, I conducted a multi-level analysis on the R&D unit, the R&D project and the professionals

as nested units of analysis. Mid-way through the second exploratory analysis, it became clear

that the focus of inquiry and action is the R&D professionals. Due to the breadth and depth of the

data, the analysis was conducted in three steps, two exploratory and one confirmatory (Charmaz,

2006; Feldman, 1995). The first exploratory analysis was conducted on the data collected in the

period before and throughout the open innovation experiment (2009 - 2010). During this period,

organizational members were sensemaking the new open innovation model; they kept repeating

that open innovation is a completely different approach to solving their R&D challenges. I,

therefore, conducted a comparative analysis of the open innovation model with their standard

R&D model. Right after I completed this phase of the analysis, going back to the field site, I was

struck by the shift from sensemaking to clear attitudes and responses. I therefore decided to

conduct the second wave of data collection with interviews more focused on the reactions,

attitudes and opinions regarding the open innovation experiment.

The second exploratory analysis was conducted during the period after the open

innovation experiment (2011- 2012). This period was characterized by tensions and

organizational drama between NASA’s organizational members surrounding the open innovation

model. I began mapping the tensions and coding every expression of emotion, opinion or

behavior regarding open innovation. Using ATALS/ti (a qualitative data-analysis program), I

coded the amount and intensity of tension around each of dimensions of comparisons between

the two models. A new in-vivo dimension code emerged around identity. This led me to conduct


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a comparative narrative analysis of the standard R&D model, and the one introduced by the open

innovation experts (Bartel and Garud, 2009; Czarniawska-Joerges, 1998). As new reaction

patterns emerged, I conducted a thematic analysis (Charmaz, 2006) of the reactions by coding

and tracking patterned expressions and behaviors using ATLAS/ti. I focused on activities of the

individuals, I analyzed what the organizational members did beyond what they said. I

categorized their activities as four different types of “knowledge boundary work”. At this stage I

tested various possibilities suggested in prior literature to explain the variation of the reactions to

open innovation such as demographic characteristics (age, gender), professional characteristics

(type of profession, professional education, level of education), organizational characteristic

(tenure, role in the organization, hierarchy), R&D unit, patents and publications, and the level of

success in finding solutions through the open innovation model. None of these factors explained

the variation in knowledge boundary work responses. With time, it became clear that the change

in the knowledge boundary work co-evolved with the change in professional identity and that

they are mutually reinforcing. These activities became the focus of the study. The last step of

analysis conducted was confirmatory across the previous periods of the study.

Findings

The Backdrop: Innovation Produced with Clearly Pre-defined & Selectively Permeable

Knowledge Boundaries

On July 20 1969, the US National Aeronautics and Space Administration [NASA] did the

impossible. Putting a man on the moon pushed the frontiers of science and engineering;

innovative by any standard. Ever since, top engineers and scientists from around the world have

been attracted to work for NASA and to be in the center of new knowledge creation and

innovation in space exploration. It has been the locus of new knowledge and innovation in space


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exploration for several decades which has led to breakthroughs in our understanding of ourselves

and our universe. In the few years prior to this study, following a period of traumatic shuttle

accidents, NASA embarked on a new and exciting scientific and technological journey, a human

spaceflight to Mars (Constellation space program). The production of innovation at NASA was

led by its R&D professionals. Work in the field of space exploration requires a high level of

expertise and specialization in one’s professional and educational background, leading to the

creation of knowledge experts in their fields. This environment created multiple knowledge

boundaries, which were reinforced by the organization structure and were the most common

topic for tension and humor in the daily life at NASA. However, R&D work was not contained

within the walls of the organization. Instead, it had a long tradition of collaboration and

contracting relationships with a network of public and private organizations. The relationship

between the participants in the R&D processes were clearly predefined through agreements and

sometimes contracts (in the case of contractors and collaborators). Therefore, there was a clear

demarcation of who is working on each portion of the project. The work was produced within

pre-defined and selectively permeable boundaries.

In 2009, organizational members across NASA worked on reinvigorating and

demonstrating their innovative capabilities. This followed the “Augustine Committee” report and

later President Obama’s administration to change NASA’s human spaceflight mission. As the

head scientist of Johnson Space Center kicked off a cross organizational meeting on a Monday

morning in that period; “For those of you who have been sleeping in the last six months,

headquarters are telling us we are not innovative enough, we need to show them they’re wrong”.

Various “innovation initiatives” sprouted up to demonstrate existing R&D capabilities.

Management encouraged bottom-up innovation, creating “innovation days” and “fairs”, and


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internal innovation contests for seed funding. The head of the engineering directorate, initiated

an ambitious project named after Morpheus, the Greek god of dreams, to land a humanoid robot

on the lunar surface in 1,000 days. This project rejuvenated the R&D organizational members

and brought back excitement and pride “Look outside the window, this is innovation!” I was told

at the day of testing Morpheus. This was the environment and driving force behind the open

innovation experiment and this study. The open innovation experiment was initiated by the head

of the Space Life Science Directorate (SLSD) at NASA. The model was introduced to the

leading scientists and engineers of their R&D groups, and together decided to experiment with

three leading open innovation platforms, Innocentive, Yet2.com and TopCoder (hereby: “the

platforms”) on their strategic R&D problems.

The Open Innovation Experiment: Challenging Knowledge Boundaries, and Yielding a

Scientific Breakthrough in an Unprecedented Speed

The open innovation model was introduced to NASA’s space life science directorate

organizational members through expert led workshops from each of the three platforms. By the

end of these workshops, they decided to conduct an experiment through open innovation

platforms and communities with the major R&D challenges of their strategy plan for that year.

The experiment was carried as in parallel to usual activities, they would work on solving these

problems through the standard R&D model processes of internal work, contracting work and

collaborating with external public and private organizations. They assumed this experiment

would take a year. In total, 14 R&D problems from 11 R&D units were posted on the various

open innovation platforms, from a variety of scientific and technological fields (ranging from

microbiology, heliophysics, mechanical engineering, radiation, material science to medical

devices and more). The process was very fast; the R&D organizational members formulated their


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problems into short challenge statements with solution criteria, and posted them for the world to

solve on the platforms for 2-3 months. The nature of these two processes was fundamentally

different. The standard R&D process is an organizational process based on negotiations between

various professional and organizational groups that spans across years (3-5 average R&D life

cycle). The open innovation model was a distributed process with participants that mostly work

virtually in short and fast pace R&D cycles (3-6 months average), following rapid prototyping

and agile programing principles. The costs of standard R&D projects are very high and induce

risk adverse approaches, whereas the open innovation model assumes multiple tasks with low

costs in an experimental mode.

After 3 months from the beginning of the experiment, while NASA’s organizational

members (about 100 individuals) were working on these 14 strategic challenging, on the open

innovation platforms, over 3,000 individuals from 80 countries tried to solve these same

problems (see appendix A). More than 300 solutions were submitted overall for evaluation of

NASA’s R&D members in the various units of the challenges. Each of these challenges had clear

solution evaluation criteria that were set before hand, scientific and technological requirements

that a proposed solution needs to meet. The proposed solution surprised the R&D by their

quality, they were successful beyond their set expectations. Some solutions even exceeded their

solution criteria and all within astonishingly short time turnaround, as a lead scientist expressed:

“In general it is known that to receive a solution for the cost (award and success fee) would not

be possible otherwise. Turnaround time for a solution like this could take years”. In particular,

there was one solution that became known as the “home run” of the open innovation experiment.

This was a solution to a well-known and researched problem in heliophysics: prediction of solar

particle events (SPEs), popularly known as solar storms. One of the most significant health risks


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to current and future human exploration of the solar system is exposure to ionizing radiation.

Therefore since the start of the space program, NASA and its collaborators in academia and

government have invested significant financial and intellectual resources towards the

development of better solar events forecasts (24 hour forecast window). After years of research,

the best algorithms in heliophysics could only predict a flare 1 to 2 hours in advance with 50%

accuracy and a two-sigma confidence interval; this challenge sought an algorithm that could

predict an event up to 4 to 24 hours in advance. This problem was posted in December 2009,

suggesting a reward amount of $30,000.

In a three-month time period, over 500 individuals expressed interest in trying to solve

this problem, 11 submitted solutions and the winning solution came from a semi-retired

telecommunications engineer from rural New Hampshire. Using only ground-based equipment

instead of the traditional use of orbiting spacecraft, he created an algorithm able to forecast solar

flare 8 hours in advance with to 75% accuracy and three-sigma confidence level, well beyond the

expected result in an order of magnitude. When the R&D organizational members at NASA

tested his solution on their operational systems they even achieved better results: 80%-85%. The

head of the R&D unit that worked on this problem was stunned: “This has spun up so fast here

and that’s what has caught everyone off guard”. The successful results of the open innovation

experiment triggered a wave of excitement and positive responses all the way from members

across the organization, the innovation team, SLSD management, NASA’s headquarters, to the

White House Office of Science and Technology. Management and the innovation team were

excited about the internal results and positive external responses and moved forward to prepare

the ground for turning open innovation from an experiment into a day-to-day reality, integrating

it with their standard R&D model.


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Challenging Knowledge Boundaries, in turn, Challenges Professional Identity: A few

months after the successful results of the open innovation initiative, the management and

innovation team assembled a larger “next steps” workshop intended to discuss the next strategic

R&D challenges for 2010-2011 and how open innovation can be used to solve them. The day

started with grandiose statement; “This puts you on the edge of many organizations in many

ways of all organizations in the world trying to figure out this open innovation perspective …”

and ended with a plea of the head of space life science to simply think of this open innovation

model as “ just another tool in your toolkit “. The tensions, debates and forces unleashed on that

day, led to a very different trajectory than that planned. The intensity of fears and resistance

expressed in the room throughout the day was out of the out of the ordinary and juxtaposing

camps of organizational members started coalescing. The management and innovation team were

utterly surprised, confused, and frustrated by the rising tensions and fragmentation. The reactions

were so strong that the strategy and innovation team decided to stop the efforts on the directorate

level and instead to conduct one on one meetings with the leading scientists and engineers of

their various units to understand these reactions.

In conversation with managers the issues that were initially raised by the R&D

professional were budget or technical issues, but these were quickly and easily solved did not

change the strength of the reactions and in some cases, resistance. In the months following this

workshop, these tensions and fears did not subside, instead clear camps were forming. On one

hand, some scientists and engineers not only continued to use the open innovation platforms and

the knowledge found through them, but were also changing their old processes to match open

innovation models. On the other hand, other scientists and engineers refused to discuss any R&D

challenge fearing it would become an open innovation challenge. The open model was posing a


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significant challenge to the R&D professionals’ identity; it triggered a wave of discussion about

who they are, why they are where they are, and whether this needs to change. During this period,

when discussing the open innovation experiment amongst themselves or when interviewed about

it, many organizational members suddenly talked about why they joined NASA, who they are

and how they were trained. Analyzing their innovation narratives, work artifacts and

organizational ceremonies, a clear common identity of the R&D professionals was evident;

namely being the “problem solver”.

This “problem solver” role identity was rooted in the education and professional training

of both scientists and engineers. The conflict with open innovation was described by Marvin, a

leading scientist and bio medical engineer: “It’s [the resistance to open innovation] really

intrinsic, the history of the scientific method goes against it…In our training, trying to solve

problems in the scientific method was: I take in all this information, I synthesis it, I do analysis

and I come to some conclusion and so to reach out to other people to solve it, it’s like cheating !”.

R&D organizational members saw themselves as problem solvers, even if the project was done

in collaboration or through contractors. In the open innovation model, they lose control and

choice of what is exposed and what solutions are pursued. This study was conducted at Johnson

Space Center (JSC), a legendary place that solved the technical problems for Apollo 13, in April

1970, when they reported “Houston, we have a problem” and was able to bring the astronauts

back to earth safely. A sense of importance was palpable in the workplace, even sometimes

ridiculed; “There is a joke about why we don’t launch the shuttle from here, because gravity is

too strong in the center of the universe… I think they are up for a real awakening…the mentality

is we are the only ones who can do it”. Culturally speaking, to shift from “Houston, we have a


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problem” to asking the world for solutions and receiving “Houston, we have a solution” was

jarring.

Emergent Professional Identity Refocusing Work: From “Problem Solvers” to “Solution

Seekers”: The open innovation model, as presented by the external experts and explained by the

managers, did not have a clear implication on the role of the scientists and engineers at NASA.

Neither the experts nor the managers really discussed these issues; their focus and attention was

on the efficiency of the model, its speed, its low costs and its impressive performance in solving

challenging R&D problems. The external experts’ narratives hardly ever mentioned the R&D

organizational members or any individuals. A narrative analysis of these open innovation texts

(from experts and internal presentations and the open innovation online platforms) finds no

individual hero, but rather glorification and credit to the “web”, the magical technology that

makes these connections and innovations possible. This lack of clarity left room for

interpretation regarding the role of the R&D professionals by those who were actually using it;

some R&D professionals perceived this challenge to their identity as a direct threat and

responded with identity protection work, while others perceived it as an opportunity to change

and enhance their identity, role and capabilities

Some professionals, such as Mike, a leading researcher expressed the perceived conflict

very clearly: “I've been attracted to places that allow you to access a problem, come up with a

plan, and execute the solution… to be able to think and solve greater problems. If I can’t do it at

NASA, what is keeping me from going somewhere else?”. Chris, a bio-medical engineer,

explained the significant shift adopting the open innovation model requires; “It’s going to take

them a change in their heads on how they do their jobs… They truly look at themselves as… the

brains behind the vehicles and they are the ones, I mean, “there isn’t anyone that’s going to know


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better what they need to go do, they are the ones, that’s what they do... and they make the best

ones”. Identity work emerged in internal meetings as the approaches clashed. One camp argued

for a protecting their current identity and solidifying their capabilities. The other argued for a

change as a highly appreciated lead scientist declared: “Your main responsibility is to seek for

solutions and they may come from the lab, from open innovation, or from collaboration, you

should not care! You are the solution seeker!”. From that meeting on, this new identity of a

“solution seeker” defined the move the camp put forward: “at the end of the day it’s about the

big agenda vs. the personal one…Science is about finding the truth!”.

Unveiling The Relationship between Professional Identity Work and Knowledge Boundary

Work: Challenging the existing model of work for R&D professionals sheds light on the

unexplored relationship between knowledge boundaries and professional identity and how

changing one leads to changing the other. The emergent professional refocusing identity work

enabled a significant change in the knowledge work and boundaries. Organizational members

who discussed the need to change their professional identity in the following months also made

an explicit effort to change their knowledge boundaries and work processes. In the traditional

R&D model, the professional identity of “problem solvers” created very clear group boundaries

of “us” and “them” in which only a bounded group of experts generated solutions. Expanding

their role to that of “solution seekers” opened these boundaries and invited everyone to generate

solutions, to be “problem solvers”. One of the engineers explicitly expressed this change in a

blog post: “The Next Rocket Scientist: YOU... Solving those mysteries has long been the domain

of lab-coat wearing scientists in government agencies and universities. However, with the advent

of the Internet, social web, and open source data, it has become possible for anyone to make

scientific discoveries about our universe. Find out how you can actively contribute to space


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exploration and how the collective power of the Internet is enabling the future of scientific

research”.

The R&D professionals who did not go through any significant change in their identity,

did not go through any change in their knowledge boundaries and work. The rejection of the

Open Innovation model was founded in identity protection. Alex, a leading scientist expressed:

“They want the opportunity to contribute to something that nobody's ever done before. And so

this (open innovation) becomes quite a slap in the face!”. When asked whether there is any

change that needs to take place in their current work, they called for increasing resources and

focus on strengthening and solidifying their current “problem solving” identity capabilities. After

this period of emergent professional identity work, organizational members transitioned from

discussing the need to change to acting upon it. The reactions ranged from fierce rejection to

enthusiastic embrace with clear patterns and groups (29% of the R&D professionals were

protecting boundaries while 42% were permeating, and the rest did not manifest any significant

boundary work). There were strong and important scientists and engineers at each end of the

spectrum; no side was stronger or more influential. Rather, these groups decreased the level of

discussion and arguments amongst themselves and simply started initiating changes in their own

labs and organizational units. The new “solution seekers adopted many open innovation model

principles significantly changing their R&D work. The identity protectors either rejected the

open innovation model or allowed for it without actually adopting the resulted knowledge.

“A Shift From Thinking That ‘The Lab Is My World’ to ‘The World Is My Lab’”: In order

to capture the change in their work as it co-evolved and becoming mutually reinforcing, I

focused on analyzing the change in their knowledge boundary permeability before and after the

open innovation experiment. I investigated the knowledge flows going in and out of the


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members’ R&D projects’ processes and found four distinctly different types of knowledge

boundary work. The change in the permeability of the knowledge boundaries was meaningful, as

was captured by one of the R&D professionals; “It is a shift from thinking about “the lab is my

world” to the lab is my world”. Table 2 summarizes and compares the knowledge boundary work

types:

---------------------------------------------------

Insert Table 2 Here

---------------------------------------------------

Knowledge Boundary Permeation: New “solution seekers”, permeated their knowledge

boundaries in the subsequent months. There were two major types of boundary work taking

place on this side of the spectrum: “boundary dismantling”, when professionals made an effort to

completely destroy the boundaries of the R&D process, and “boundary perforating”, when

professionals dismantled selective important parts of these boundaries.

Knowledge Boundary Dismantling: Knowledge boundary dismantling entailed an attempt to

destroy all the knowledge boundaries of the standard R&D process. Some of these members

worked to change their current R&D units, while others left their original units to create a new

unit named “open NASA”, and completely devoted their time and resources to making this

change a reality. All these members believed that the R&D model should change into an open

one. To begin with, these members worked on opening more of their internal knowledge

workflows and strategic R&D problems to the external world. They took their existing and

emerging R&D challenges and opened them to essentially anyone to consume or contribute to.

When a new strategic and urgent R&D challenge emerged and the immediate automatic

approach was to treat it as a “top secret”, experts-led R&D project; the lead scientist (who coined

the need to shift from “problem solvers” to “solution seekers”) and the professionals in her


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group, worked hard to dismantle its boundaries. The change in the knowledge flows was

significant, in the definition, data collection, and evaluation phases. These professionals did not

simply embrace the open innovation model as presented by the external experts to the

organizations, focused on posting well-defined problems. Instead, they adopted the principles of

the original “open source” model, seeking for openness and transparency throughout the full

process, as one scientist expressed: “if we only open one end of the process, we will still have

our bias on the other end”. These R&D professionals fully utilized and integrated the knowledge

found through open innovation platforms. They dedicated special attention to thinking about

what will they do with the solution found. Several engineers even decided to found the first

international space apps hackathon (a popular format in open source communities) which went

on to become the largest international hackathon event in 2012 (and is taking place ever since

annually). The boundary dismantling work of these R&D professionals shifted the locus of

knowledge and innovation produced outside the previously pre-defined boundaries. Knowledge

production was conducted within undefined and permeable boundaries and was regularly

flowing both inside out and inside in. A prime example of the shift was building a publicly open

wiki as both the internal knowledge management tool and the way to bring external knowledge

in. The wiki included every new and relevant knowledge work, and as an open wiki was

automatically shared with other members from three different space centers across the country,

and with the whole world.

Knowledge Boundary Perforating: In this boundary permeation work, R&D professionals only

dismantled selective parts of their R&D process’ knowledge boundaries. They perforated

multiple “holes” in the boundaries of the R&D process for knowledge flows in and out, mostly at

its outset or before important milestones. Not seeing the need to make a discrete choice between


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either the standard or the open R&D models, they concluded that the debate over the “best

model” of their colleagues is misguided: “So then one barrier [to adopting open innovation] or

one problem might be assuming that one size fits all!“. They decided that the outset of every

project should be open. Tom, a discipline scientist explained: “It's [using open innovation] going

to get you out of your bubble a little bit”. These R&D professionals fully integrated the

knowledge found through open innovation platforms in their R&D process. For instance, the

food packaging material that was found through the open innovation experiment, was shipped

from the solver in Europe and tested in the food lab. In response to criticism they argued that

they fully integrating the knowledge from outside, treating it with the same diligence in the

quality testing process they use for internally developed solutions.

Knowledge Boundaries Protection: Organizational members who protected their professional

identity as “problem solvers” also protected their knowledge boundaries during this period.

There were two major types of boundary work taking place on this side of the spectrum: false

boundary permeating and boundary fencing.

False boundary Permeation: This boundary work was illusive; initially these R&D

professionals seemed to be permeating their boundaries as they allowed for opening some

challenges. However, a closer investigation of these professionals’ work revealed that there was

no significant change in their identity or knowledge work. Ultimately the fruit of the open

challenges was simply discarded and not integrated. They were actually adding an additional

boundary. Internal knowledge was very sparingly shared, only the knowledge needed to write the

challenge description on open innovation platforms and only such knowledge on non-strategic

projects. The participation was an illusion performed for management as Mike, a discipline lead

scientist, explicitly explained: “We quickly pick something we thought that would give some


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results so that we could, you know, keep that person happy for a while and go and address the

twelve other things we're working on.” They intentionally blocked the gained external

knowledge from any use even though some of the solutions had substantial scientific and

technological potential for their work and the space mission. The solutions found, the ostensible

goal of the members’ work, were discarded. In one lab, it went as far as withholding the fact that

the lab was actually participating in open innovation.

Boundary Fencing: Some R&D organizational members vehemently protected their identity as

“problem solvers” and their knowledge production process. They interpreted the open innovation

model as a clear threat to their professional identity; as expressed by Matt, a senior scientist;

“when we see opportunities like Innocentive, It's extremely frustrating…of what value am I?”.

These professionals protected their knowledge boundaries by fencing off the knowledge gained

through the open innovation experiment as did those who falsely permeated, but went further.

They actively rejected any attempt to allow knowledge flows in or out of the predefined

boundaries, and where their falsely permeating colleagues pretended to embrace open

innovation, they made no such theatre. Many went as far as to dissuade colleagues from

participating. When asked about the successful results of the open innovation experiment, these

organizational members dismissed it as unrelated to their field; “that’s a heliophysics specific

thing”.

Epilogue: Innovation Produced With Permeated Boundaries

These different types of boundary work took place over the following two years, with strong and

influential scientists and engineers were on both sides of the spectrum. The change in the R&D

work after boundary dismantling became increasingly evident with time and the gap between the

traditional model and the open one grew deeper. One of the organizational members explained


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“The open source mindset transfers us from the innovation-resistant Not Invented Here attitude

to Proudly Found Elsewhere.” while a colleague, when asked, “what do you think about open

innovation?” he answered: “You see this? [pointing to a pile of scientific articles written by his

group], read this, this is innovation, this is research, and not gimmicks”.

---------------------------------------------------

Insert Table 5 Here

---------------------------------------------------

The longitudinal design of this study enables analyzing the relationship between the

knowledge boundary change and professional identity change over time. This analysis brings

forth a mutually reinforcing relationship between the two processes; they feed and strengthen

each other. The more boundary permeation took place, and new work practices emerged, the

more meaning and legitimacy the refocused identity received. Boundary permeation required

building capabilities focused on problem formulation, distant search, evaluation and integration

(see discussion section below). The identity shift was evident in the emerging new innovation

narrative, namely, the solution seeker as the hero. In these emergent innovation stories, the

organizational member was praised for finding a solution in a creative way. One new and

popular innovation story was about an R&D professional (engineer) who found a needed medical

device, which was found suitable for the international space station, through Google and

YouTube. In this innovation story, the creativity and resourcefulness of the R&D professional

was highly valued, rather than the raw creative prowess. Innovation was re-narrated.

Discussion

Organizing for Innovation and The Changing Role of R&D Professionals

This study sheds a timely light on the broader and long-studied question of how to organize for

innovation. Since Schumpeter (1934), social scientists have been modeling and arguing about the


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optimal way to organize innovation (see Baldwin and Von Hippel, 2011). The open innovation

model holds a potential to increase innovation performance on one hand, (Benkler, 2006;

Jeppesen and Lakhani, 2010; Lerner and Schankerman, 2010) but it goes against major

assumptions of the existing models for innovation in organizations (see Lakhani et al., 2013).

The results of this study suggest that, indeed, the web-enabled open innovation model introduce

a real option for advancing scientific and technological knowledge, it can lead to the production

of scientific and technological breakthroughs under unprecedented time and resource constraints.

However, at the same time, it poses a significant challenge to the professional identity and

knowledge work and capabilities of R&D professionals in organizations today. Adopting such a

model requires a major shift in the knowledge work and professional identity of the R&D

professionals.

The shift from problem solvers to solution seekers has major implications on the role of

scientists and engineers in the production of knowledge and scientific and technological

innovation. History has witnessed several shifts in the role of scientists and engineers in

organizations (Bailyn, 1980; Merton, 1973; Mokyr, 2002; Shapin, 2008) and this study illustrates

the potential paths the roles can follow in the future. This shift has important implications for

innovation policy (Evans, 2009; Janasoff, 2004; Murray et al., 2012) all current resource

allocation, incentive and award systems, and education and professional training are devoted to

problem solving and the related capabilities and skillset and not to solution seeking (Azoulay et

al., 2011; Jones, 2009; Stern, 2004).

New skills would stresses the capabilities and practices of formulating problems,

searching for solutions, evaluating solutions, and integrating knowledge. Each of these

capabilities and practices warrants future work: Problem formulation has been studied to be a


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highly political and challenging process within organizations (Baer, Dirks and Nickerson, 2013;

Lyles and Mitroff, 1980). The integration of external knowledge is expected to run into

challenges of absorptive capacity and combinative capabilities (Cohen and Levinthal, 1990;

Kogut and Zander, 1992; Zahra and George, 2002). Therefore, this shift offers fertile ground to

test, challenge and contribute to many of our existing theories regarding knowledge work

processes and capabilities (Afuja and Tucci, 2011; Bailey, Leonardi and Chong, 2010; Felin and

Zenger, 2014; Levina and Fayard, 2012; Lifshitz-Assaf, 2015).

Permeation of Knowledge Boundaries

Investigating the different types of “knowledge boundary work” advances our understanding of

the changes in the nature of knowledge work within organizations that the Internet has brought

about (Argote etal., 2003; Levina and Vaast, 2008; Laursen and Foss, 2012; Murray and

O’Mahoney, 2007). The R&D professionals no longer function merely as knowledge gate

keepers; they can also perforate and dismantle existing knowledge boundaries in order to let

internal knowledge flow out and external and for distant knowledge to flow in via online

platforms and communities. An in depth investigation of the work of professionals; analyzing

their changing work flows, tasks documents, observing project meetings and practices enabled

the inquiry of the actual knowledge work shift. This type of findings suggests a resolution to the

fiercely debated novelty of the open innovation phenomenon (Cassiman and Valentini, 2013;

Chesbrough et al., 2014; Trott and Hartman, 2009). The term “open innovation” is currently used

to describe such a wide range of knowledge creation and innovation processes at organizations

(Dahlander and Gann, 2010) that it has become unclear which innovation processes qualify as

“open” and, more critically, how this approach differs from the traditional one (Helfat, 2006;

Gann, 2005; Murray and O’Mahoney, 2007). Focusing on the knowledge boundary work and the


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change of knowledge boundaries of the R&D process contributes clarity to the topic and its

fuzzy terms.

Furthermore, this study’s findings raise broader questions regarding the knowledge

theory and boundaries of the firm (see Eisenhardt and Santos, 2005). Until recently, the

prevailing consensus among various streams of theoretical and empirical literature in strategy,

economics and organizational theory has been that innovation does and should take place within

the boundaries of the firm. However, there is a call in the literature to re-examine our theoretical

assumptions regarding the boundaries of the firm due to the change in costs and structure of

knowledge (Baldwin and Von Hippel, 2011; Felin and Zenger, 2014; Leonardi and Bailey, 2008;

Lakhani at al., 2003; Laursen and Salter, 2014). Our current theoretical frameworks fall behind

the change in the field (Felin and Zenger, 2014; Murray et al., 2012). It is a prime time to

advance both our theoretical and empiric understanding of the mechanisms and boundary

conditions of open models across various domains of problem solving, professions and

organizations.

Unveiling the Relationship Between Knowledge Boundary Work and Identity Work

This study sheds light on the identity refocusing process through which professionals are able to

change their boundaries instead of protection or spanning across them. They can perforate and

dismantle their boundaries in such a way that enables other, external social actors to bring in

knowledge and build upon the internally produced knowledge. They are able to do so by

reconstructing their professional identity, role and capabilities. For instance the professions of

education and journalism that are currently challenged to open their boundaries via the web-

based models. As professors’ profession is being challenged by open education (Friedman,

2013), based on this study, we can predict that professors that will go through a refocusing and


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reconstruction of their identity and capabilities will permeate their boundaries and let external

knowledge (other professors) go into their knowledge work with their students and open their

internal knowledge work (their teaching classes and notes) to others. Future work should analyze

multiple professions to investigate the boundary conditions of the mechanism found in this study.

Most studies of knowledge and innovation processes do not investigate the identity of the

members involved and its impact. This study builds on the notable exceptions that examine the

effect of pre-hoc organizational identity and technological framework on resistance to

technological change (Benner and Tripsas, 2012; Kaplan et al., 2003; Kaplan and Tripsas, 2008;

Tripsas, 2009). It expands our understanding by investigating the individual level and the case of

change. The findings strengthen that dynamic view of identity (Pratt et al., 2006; Kreiner et al.,

2006) by finding that professional identity work enables dealing with a tension and a contentious

change in work. It also emphasizes that active and important role of professional and their

meaning making (Weick, 1995) processes and re-narrating capabilities (Ibarra and Barabulescu,

2010). This study suggests an independent contribution to the identity literature since there is

little research on identity processes, let alone to their relationship with other processes in the

organization (Gioia et al., 2010; Glynn, 2000; Pratt, 2012; Pratt, 2006; Ravasi and Schults, 2006;

Tripsas, 2009; Tripsas and Gavetti 2000). Having a longitudinal study design enabled

investigating the shift of the professional identity of the R&D professionals at NASA over time.

The relationship between knowledge boundaries and identity in a case of change suggests

that there is a less observable relationship between the two constructs when they are in a stable

state (Lave and Wenger, 1991; Kane, 2010). Future work should investigate this relationship; for

instance do we see more boundary spanning or cross-disciplinary work of professionals who

have a more expanded defined professional identity? This has major implications on increasing


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cross-disciplinary collaboration in developing science and technology (Fleming et al., 2007;

Hargadon and Bechky, 2006; Wutchy et al., 2007). In times where specialization is rising (Jones,

2009) and the fruits of scientific are becoming harder to produce within the disciplinary fields,

such focus on developing a more expanded professional identity could provide an answer to the

production of innovation. Finally, these findings illuminate the equivocal nature of technology

(Weick, 1990). Scientific and technological knowledge always walked the fine line between

being democratic or elitist, belonging to a privileged few, or empowering the masses (Gieryn,

1983). Technology has dramatically altered this tension and currently enables the

democratization of knowledge in unprecedented breadth (Dyson, 1999). It now remains for the

professional, sociological and organizational forces to determine the nature of this change.

Tables and Graphs

Table 1: Demographics of Organizational Members at NASA

Total n = 98

Educational Background

Science (percentage) 37 (37.8)

Bio-Medical Engineering (percentage) 8 (8.2)Engineering (percentage) 30 (30.6)Medicine (percentage) 9 (9.2)Other (percentage) 14 (14.3)Male (percentage) 62 (63.3)Female (percentage) 36 (36.7)Average age in years (standard deviation) 41 (8)Average tenure in years (standard deviation) 13 (8)

Table 2: Knowledge Boundary Work Types and Implications

The type of knowledge boundary work Internal

knowledge

External

knowledge Resultingknowledge boundaries

Boundaries

illustration Resultinglocus ofknowledge &

innovation

production

Boundary permeation

Dismantling Mostly open Fullyintegrated

Undefined &

Permeable

boundaries

Outside the

pre-defined boundaries


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Perforating Significant part is open

Fully

integrated

Semi

permeable boundaries

Either inside

or outside

pre-defined boundaries

Boundary

protection

False-Permeating

Significant

part keptinside

Isolated False semi- permeable

Inside

predefined boundaries

Fencing Kept inside Kept outside Clearly predefined &selectively

permeable

Table 3: Shift in Knowledge and Innovation Production Locus

Knowledge and

innovation production locus

Within pre-defined boundaries With permeable boundaries

R&D professionalidentity and role

Problem solvers Solution seekers

Process Participants Experts (from inside and outsidethe organization)

Anyone (can be anonymous)

Type of process Organizational process,negotiation based

Distributed process, virtual and“light” communication andinteraction

Level of control High (knowledge production process with ongoing control

and testing milestones)

Low (knowledge produced bythe external solvers, tested only

after production)Spatial dimension Geographically concentrated inone or few countries

Widely geographicallydistributed

Temporal dimension Long R&D cycles (3-5 years) Short R&D cycles (3-6 months)

APPENDIX A: The geographic spread of solvers that tried solving NASA’S R&D problems at

the open innovation experiment: 3,000 solvers from 80 countries

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