coordinating knowledge creation in multidisciplinary ...

Academy of Management Journal

COORDINATING KNOWLEDGE CREATION IN MULTIDISCIPLINARY TEAMS: EVIDENCE FROM EARLYSTAGE DRUG DISCOVERY

Journal: Manuscript ID: Manuscript Type:

Academy of Management Journal AMJ-2013-1214.R3 Revision

Keywords:

Longitudinal < Research Design < Research Methods, Case < Qualitative Orientation < Research Methods, Coordination < Group/team processes < Organizational Behavior < Topic Areas, Innovation processes < Technology and Innovation Management < Topic Areas, Design/Structure < Organization and Management Theory < Topic Areas, Knowledge management < Organization and Management Theory < Topic Areas

Abstract:

Based on a multi-year field study of early-stage drug discovery project teams at a global pharmaceutical company, this paper examines how multidisciplinary teams engaged in knowledge creation combine formal and informal coordination mechanisms when faced with unpredictable interdependencies among specialists’ knowledge domains. While multidisciplinary teams are critical for knowledge creation in increasingly specialized work environments, the coordination literature has been divided with respect to the extent to which such teams rely on formal coordination structures and informal coordination practices. Our findings show that when interdependencies among knowledge domains are dynamic and unpredictable, specialists design self-managed (sub-)teams around collectively held assumptions about interdependencies based on incomplete information (conjectural interdependencies). These team structures establish the grounds for informal coordination practices that enable specialists to both manage known interdependencies and reveal new interdependencies. Newly revealed interdependencies among knowledge domains, in turn, promote structural adaptation. Drawing on these findings, we advance an integrative model explaining how team-based knowledge creation relies on the mutual constitution of formal coordination structures and informal coordination practices. The model contributes to theory on organizational design and practice-based research on coordination in cross-disciplinary knowledge creation.

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Coordinating Knowledge Creation in Multidisciplinary Teams: Evidence from Early-stage Drug Discovery Shiko M. Ben-Menahem [email protected] Georg von Krogh [email protected] Zeynep Erden [email protected] Andreas Schneider [email protected]

Department of Management, Technology, and Economics ETH Zurich Weinbergstrasse 56/58 8092, Zurich

Authors contributed equally. This research was supported in part by a grant from the Swiss National Science Foundation (SNF 100018-146439). We thank Scott Sonenshein and three anonymous reviewers for their insightful comments and suggestions throughout the review process. We gratefully acknowledge Natalie Reid for her editorial assistance and Fabienne Vukotic for her research assistance. We also thank Vivianna He, Pursey Heugens, Johannes Meuer, Michaéla Schippers, Nicole Rosenkranz, Christian Wedl, as well as participants of the faculty seminar series (Professor Tobias Kretschmer) at LMU Munich for their feedback on earlier drafts of this work. We further express our gratitude to the participants in this study.


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2 COORDINATING KNOWLEDGE CREATION IN MULTIDISCIPLINARY TEAMS: EVIDENCE FROM EARLY-STAGE DRUG DISCOVERY

ABSTRACT Based on a multi-year field study of early-stage drug discovery project teams at a global pharmaceutical company, this paper examines how multidisciplinary teams engaged in knowledge creation combine formal and informal coordination mechanisms when faced with unpredictable interdependencies among specialists’ knowledge domains. While multidisciplinary teams are critical for knowledge creation in increasingly specialized work environments, the coordination literature has been divided with respect to the extent to which such teams rely on formal coordination structures and informal coordination practices. Our findings show that when interdependencies among knowledge domains are dynamic and unpredictable, specialists design self-managed (sub-)teams around collectively held assumptions about interdependencies based on incomplete information (conjectural interdependencies). These team structures establish the grounds for informal coordination practices that enable specialists to both manage known interdependencies and reveal new interdependencies. Newly revealed interdependencies among knowledge domains, in turn, promote structural adaptation. Drawing on these findings, we advance an integrative model explaining how team-based knowledge creation relies on the mutual constitution of formal coordination structures and informal coordination practices. The model contributes to theory on organizational design and practice-based research on coordination in cross-disciplinary knowledge creation.

Key words: coordination, organizational design, practice, knowledge creation, task uncertainty, interdependence, multidisciplinary teams, drug discovery, research and development.

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3 Organizations increasingly rely on multidisciplinary teams for creating high-yielding knowledge1 at the frontier of science and technology (e.g., Hoegl & Gemuenden, 2001; Singh & Fleming, 2010; Wuchty, Jones, & Uzzi, 2007). Understanding how such teams effectively coordinate knowledge creation across knowledge domains—i.e., manage interdependencies among specialists—is a subject of growing importance (e.g., Bruns, 2013; Kotha, George, & Srikanth, 2013; Majchrzak, More, & Faraj, 2012; von Krogh, Nonaka, & Rechsteiner, 2012). As the complexity of scientific problems increases, and as efficiency gains from cumulative learning drive individuals to specialize (Becker & Murphy, 1992; Jones, 2009), multidisciplinary teams increasingly face unpredictable interdependencies among specialists due to high task uncertainty (i.e., incomplete information about tasks) (Argote, Turner, & Fichman, 1989; Cardinal, Turner, Fern, & Burton, 2011; Gardner, Gino, & Staats, 2012). While it is well established that such conditions pose massive challenges to coordination of knowledge work (Ancona & Caldwell, 1992; Cronin & Weingart, 2007; Dougherty, 1992; Faraj & Sproull, 2000; Mell, van Knippenberg, & van Ginkel, 2014; Vural, Dahlander, & George, 2013), scholars have developed conflicting theories about the role of formal and informal coordination mechanisms in knowledge creation. Some studies, building on a long tradition of organizational design research (Galbraith, 1973; Tushman & Nadler, 1978) and structural contingency theory (Burton & Obel, 2004; Donaldson, 2001; Thompson, 1967), propose that formally designed team structures are necessary for effectively coordinating knowledge intensive work. Such structures specify the grouping and 1

We apply Liebeskind’s (1996) definition of knowledge as “information whose validity has been established through tests of proof” (p. 94). The main purpose of creating knowledge through tests of proof is to reduce uncertainty and enhance an actor’s capacity to act (Huber, 1991). Because the validity of new knowledge can only be established after the fact, knowledge creation is a fundamentally uncertain endeavor (Liebeskind, 1996). Organizational knowledge creation refers to “the process of making available and amplifying knowledge created by individuals as well as crystallizing and connecting it to an organization’s knowledge system” (Nonaka & Von Krogh, 2009: 635). This definition emphasizes that, in an organizational context, knowledge creation hinges on social interactions and collaboration between individual actors (e.g., Grant, 1996; Kogut & Zander, 1992; Nonaka, 1994).


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4 linking of interdependent organizational members and prioritize information-processing interactions (e.g., March & Simon, 1958; Puranam, Raveendran, & Knudsen, 2012), thus promoting team members’ ability to coordinate their activities (Bresman & Zellmer-Bruhn, 2013; Bunderson & Boumgarden, 2010). In contrast, a growing number of studies adopting a practicebased perspective (Orlikowski, 2000) focus on the coordination of knowledge work in teams as informally emerging patterns of interactions enacted through specialists’ everyday practices (e.g., Faraj & Xiao, 2006; Hargadon & Bechky, 2006; Kellog, Orlikowski, & Yates, 2006). These studies generally suggest that formally designed coordination structures may stifle knowledge creation (Okhuysen & Bechky, 2009). The discrepancy between these two perspectives is symptomatic of a more fundamental disconnect in the literatures on the formal and informal aspects of coordination (e.g., Gulati & Puranam, 2009; Soda & Zaheer, 2012).2 While scholars have noted that this disconnect limits our understanding of how organizations function (e.g., McEvily et al., 2014; Okhuysen & Bechky, 2009), there is a lack of in-depth analysis of the process through which formal coordination structures and informal coordination practices interact in multidisciplinary knowledge-creating teams. Without taking these interactions into account, theory on how teams coordinate knowledge creation remains incomplete. In particular, it is unclear not only how formal structures evolve when designers of team structures face unpredictable and changing interdependencies among knowledge domains (Grandori & Soda, 2006; Hülsheger, Anderson, & Salgado, 2009; Puranam & Raveendran, 2013) but also how informal coordination practices are shaped by such evolving formal structures (e.g., McEvily et al., 2014).

2

A similar debate on formal and informal aspects of organization appears in a related stream of literature on organizational control (e.g., Cardinal, Sitkin, & Long, 2004; Sitkin, Cardinal, & Bijlsma-Frankema, 2010).

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5 This paper develops theory on how multidisciplinary teams engaged in knowledge creation combine formal and informal coordination mechanisms when faced with unpredictable interdependence among specialists’ knowledge domains (Grant, 1996; Thompson, 1967). We do so by using a longitudinal qualitative study of early-stage drug discovery teams at DrugCo3, a global research-driven pharmaceutical company. Our analysis illuminates important relationships between formal structures and informal coordination practices. To disaggregate the knowledge creation process and manage the integration of specialists’ efforts, multidisciplinary project teams delegate responsibility for knowledge creation to dynamic sub-team structures based on conjectures about interdependencies among knowledge domains. Within these structures, team members enact their responsibility for knowledge creation through a set of relatively stable informal coordination practices, which we conceptualize as anticipatory conforming, workflow synchronizing, and cross-domain triangulating. From our findings, we develop an integrative model suggesting that formally designed coordination structures and informal coordination practices are mutually constitutive, thus jointly enabling specialists in multidisciplinary teams to coordinate their knowledge creation efforts. The model provides new insights into how formal team structures establish the grounds for informal practice-based coordination of specialists’ knowledge creation activities (e.g., Bruns, 2013). It also develops an understanding of how specialized team members’ everyday coordination practices lead them to continuously uncover new interdependencies among knowledge domains. These newly revealed interdependencies necessitate a reallocation of responsibilities for knowledge creation, thus driving project team members to redesign formal team structures. In advancing this model of how multidisciplinary teams coordinate the knowledge creation process,

3

Names of the organization, projects, and individuals are pseudonyms.


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6 our study offers a much-needed integration of theory on formal and informal coordination mechanisms (McEvily et al., 2014; Okhuysen & Bechky, 2009).

LITERATURE REVIEW Relevant theory and research on coordination of knowledge creation in multidisciplinary teams can be grouped in two perspectives (McEvily et al., 2014; Okhuysen & Bechky, 2009). First, the organizational design perspective suggests that the division of labor in teams with specialists representing distinct knowledge domains necessitates the integration of individual efforts, giving rise to interdependence among specialists (e.g., Burton & Obel, 2004; Lawrence & Lorsch, 1967; Thompson, 1967). Building on classic information processing theory (Galbraith, 1977), organization design scholars argue that the coordination of efforts from interdependent specialists relies on formal structures4 that mandate individuals’ information provision, relationships, roles, and responsibilities. Puranam et al. (2012) posit that formal structures enable coordination by grouping and prioritizing interactions among organizational members with epistemic interdependence—that is, members whose optimal actions depend on their capacity to predict what other members will do. Thus, by directing information exchange between interdependent members and enabling them to predict one another’s actions, formal structures allow specialized team members to better integrate their individual effort and prevent coordination failures. Early organizational design literature poses a sharp trade-off between the benefits of formal structures in driving efficiency and control, and the benefits of informal, emergent relationships in driving creativity and innovation (Burns & Stalker, 1961; Gittell, 2002; Scott, 1987.

4

Although scholars have adopted diverse definitions of structure, we follow Davis, Eisenhardt, & Bingham (2009), who conclude that prior definitions “all share an emphasis on shaping the actions of organizational members,” so that “entities are more structured when they shape more activities of their constituent elements and thus constrain more action” (2009: 415).

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7 Underlying this perspective is a core assumption in information processing theory that an organization’s structural design should align the organization’s capacity for information processing with the needs for information exchange (Galbraith, 1977; Tushman & Nadler, 1978). In line with this view, organization design scholars proposed a structural contingency theory (Donaldson, 2001; Perrow, 1967; Thompson, 1967) stipulating that as task uncertainty increases and interdependencies among specialists become less predictable, structural designs with higher capacities for information processing based on more extensive interaction become more appropriate (e.g., Argote, 1982; Burns & Stalker, 1961; Sherman & Keller, 2011). Some scholars, however, cast doubt on this trade-off, suggesting that formal coordination structures may benefit knowledge creation in uncertain contexts (e.g., Cardinal, 2001; Jelinek & Schoonhoven, 1990; McGrath, 2001; von Krogh et al. 2012). This argument has strong support from research demonstrating that formal structures prompt team members to engage in joint problem-solving and helping behaviors, thus enabling the team to more effectively accomplish highly uncertain tasks with high information-processing needs (e.g., Edmondson, 1999, 2003). These studies also suggest that formal structures enable teams to innovate by significantly increasing the potential for information sharing among its members (Bunderson & Boumgarden, 2010) and promoting learning in self-managed teams facing non-routine tasks (Bresman & Zellmer-Bruhn, 2013). Such findings are consistent with a more general social science notion that formal structural design may enable coordination, communication, and exchange of knowledge among group members (Lamont & Molnár, 2002). The second stream of research on coordination takes a practice perspective, emphasizing the importance of the informal emergent aspects of coordination (Okhuysen & Bechky, 2009). This perspective challenges earlier design approaches, such as structural contingency theory, for suggesting that coordination necessitates formal structural designs based on predefined


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8 interdependencies (Faraj & Xiao, 2006). A fundamental argument is that a focus on formal coordination structures obscures the increasing demands placed on members of post-bureaucratic organizations, i.e., dealing with unplanned contingencies and emergent interdependencies (Kellogg et al., 2006). In contrast to the design perspective, the practice perspective concentrates on coordination practices as they unfold. It thus emphasizes the need for a dynamic understanding of emergent, adaptive coordination in teams engaged in knowledge work (Okhuysen & Bechky, 2009). By exploring coordination practices in the context of high task uncertainty, widely distributed expertise, and fluid interdependencies, practice studies adopting a dynamic view of coordination add important insights into how teams integrate specialist knowledge (e.g., Bruns, 2013; Majchrzak, More, & Faraj, 2012). For example, in a study of expertise coordination in medical trauma teams facing high uncertainty from fluctuating patient arrival rates, Faraj and Xiao (2006) show that complex and highly interdependent medical work relied on emergent, partially improvised coordination practices. Similarly, Bechky and Okhyusen (2011) show that for unexpected events, police SWAT teams and film production crews coordinated expertise by flexibly shifting roles, reorganizing routines, and reassembling their work. Kellogg et al.’s (2006) study of a web-based marketing organization shows that by enacting a “trading zone,” specialists coordinated their work across the boundaries of their professional communities. Within these zones, they made their work “visible, legible, and aligned” when facing an uncertain context. Thus both the organizational design and practice perspectives offer key insights into the coordination of knowledge work in team-based structures and the contingency effect of unpredictability. Yet, as recent literature reviews show (Kilduff & Brass, 2010; McEvily et al., 2014; Okhuysen & Bechky, 2009), research has focused on either the formal or informal aspects of coordination, whereas “the mechanisms describing the interplay between formal and informal

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9 elements are less well-understood” (McEvily et al., 2014: 335). As a result, the literature presents largely static and fragmentary insights into how specialists coordinate the knowledge creation process across knowledge domains. Specifically, formal structural designs appear critical for developing predictive knowledge among specialists in multidisciplinary teams, and thus for coordinating the integration of their efforts (Puranam et al., 2012). However, the design literature has largely overlooked the process whereby coordination unfolds when interdependencies among specialists are partly unknown and change unpredictably (Grandori & Soda, 2006; Puranam & Raveendran, 2013; Sherman & Keller, 2011). Practice-based research offers important insights into how emerging interdependencies are informally managed under uncertainty (e.g., Bechky & Okhuysen, 2011; Faraj & Xiao, 2006). Research in this tradition, however, has paid little attention to the structural context in which coordination practices unfold, and overlooks the possibility that existing formal structures may not only inhibit but also support the integration of specialists’ efforts under a variety of unpredictable circumstances (e.g., Brass, Galaskiewicz, Greve, & Tsai, 2004; Hollenbeck, Ellis, Humphrey, Garza, & Ilgen, 2011; Jelinek & Schoonhoven, 1990; Pennings, 1992). In line with McEvily et al.’s more general observation that “it is essential to clarify the conditions under which formal and informal elements [of organizations] interact” (2014: 333), this study draws on longitudinal qualitative fieldwork to illuminate how formal coordination structures and informal coordination practices co-evolve. In so doing, we contribute a more comprehensive understanding of how knowledge creation is coordinated within multidisciplinary teams facing unpredictable interdependencies. RESEARCH DESIGN Case Selection and Overview


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10

We conducted a single-site case study (Yin, 2003), which allows for in-depth qualitative investigation of the coordination process. Our study focused on early-stage drug discovery projects at DrugCo, a global pharmaceutical company ranked among the top 10 firms as measured by revenues and global market share for therapeutic drugs. Drug discovery occurs in the first five to six years of a 12- to 15-year development trajectory from the lab to the market (Pammolli, Magazzini, & Riccaboni, 2011) and accounts for about one third (i.e., about $800 to 850 million) of the total cost of bringing a new drug to market (Paul et al., 2010). Decisions made during this stage, such as which disease targets to study, are critical for the overall success of commercializing a drug (e.g., Munos, 2009; Rishton, 2005). Our study of early-stage drug discovery project teams at DrugCo was guided by our interest in learning how knowledge creation is coordinated in a context where intense task uncertainty involves unpredictable interdependencies among knowledge domains. DrugCo is a prototype of large pharmaceutical companies with in-house R&D capabilities in the early exploratory and preclinical stages of drug discovery research. Modern early-stage drug discovery hinges on the efforts of multidisciplinary project teams where specialists from diverse knowledge domains need to continually coordinate their knowledge creation (Drews, 2000; Sams-Dodd, 2005). Table 1 gives an overview of typical drug-discovery knowledge domains. *** INSERT TABLE 1 ABOUT HERE *** This site also displays pervasive and inherent task uncertainty, because drug-discovery specialists focus on what is unknown about the behavior of chemical compounds and the biological causes of a disease (Dougherty & Dunne, 2012; Grandori, 2010; Northrup, 2005). A critical concern in drug discovery is that of minimizing side effects. Under the precautionary principle (Aven, 2011), pharmaceutical companies are accountable for proving that new drugs

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11 will not cause the public significant harm. Scientists’ everyday work must therefore be guided by a strong sense of obligation to practice caution in the context of task uncertainty. Thus we expected that this extreme setting would enable us to elaborate theory (Lee, Mitchell, & Sablynski, 1999) on the mechanisms whereby coordination of knowledge creation across interdependent specialized knowledge domains unfolds in multidisciplinary teams. Due to a personal contact of one of the authors with a senior executive at DrugCo, we received full access to examine everyday work in early-stage drug discovery teams. This access is unique because much of the knowledge creation precedes patenting; early-stage drug discovery processes are thus typically off limits to outside field researchers. Data Collection Our early discussions and interviews with key informants showed us that developing an indepth and rich understanding of how knowledge creation is coordinated required the use of purposeful sampling (Patton, 2002) of informants within drug discovery projects with different conditions. Drawing on initial interviews and informal discussions with accessible senior managers, and reviews of the literature on knowledge creation, coordination, and scientific research, we sampled informants from five project teams with maximum variation along four salient conditions: (1) novelty of the target, (2) stage of the trajectory, (3) therapeutic area, and (4) number of research sites. First, novelty of the target refers to the extent to which the link between a biological target (e.g., the disease-causing gene) and a final medical indication is understood at the outset of the drug discovery project. A target is novel if little is known about how a drug molecule “modulates” it (Eder, Sedrani, & Wiesmann, 2014).5 Novelty may influence the length of the process (Sams-Dodd, 2005) and shape scientists’ ability to foresee interdependencies (Carlile, 5

We assessed novelty of the target by consulting company specialists and analyzing the portfolio of past projects.


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12 2004) through both the availability of external information, such as relevant reports and peerreviewed papers (Edwards, Isserlin, Bader, Frye, Willson, & Yu, 2011), and the extent to which scientists benefit from past in-house experience (Bresman, 2013). Second, as the drug discovery process typically lasts five to six years (Pammolli et al., 2011), we selected teams conducting research activities at each stage of the drug discovery trajectory, from the early identification and validation of the target to later stages focused on identifying, characterizing, and optimizing compounds involving potentially new drug molecules (Sams-Dodd, 2005). Selecting projects along these different stages also increased variance among team members’ experience of working together. Such experience might impact how effectively they coordinate drug discovery (Kotha et al., 2013). Third, distinct therapeutic areas achieve different clinical success rates (Booth, Glassman, & Ma, 2003). While success rates cannot be reliably predicted, differences could reflect varying levels of uncertainty in the underlying science (Ringel, Tollman, Hersch, & Schulze, 2013). Fourth, previous studies suggest that coordination effectiveness in geographically co-located teams may differ from those in dispersed teams (e.g., Pinto, Pinto, & Prescott, 1993; Srikanth & Puranam, 2011). Number of research sites indicates the co-location of project team members. Colocation can shape coordination by enabling a richer dialogue (Carlile, 2002), facilitating situated learning processes (Tyre & von Hippel, 1997), and influencing how team members mutually adjust their work (Okhuysen & Bechky, 2009). Table 2 summarizes the five projects along these dimensions, with examples of core scientific questions. ***INSERT TABLE 2 ABOUT HERE*** Data collection occurred in real time from spring of 2011 until early winter of 2012. As we progressed in our data collection and analysis, we gradually developed a sense of how project teams members coordinated the knowledge creation process. Building on our emerging insights,

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13 we used purposeful sampling to select informants from all hierarchical levels (project team leaders, senior investigators, investigators, and research associates) and all specializations across each of the three drug discovery departments: therapeutic indication (e.g., pre-clinical safety), chemistry (e.g., molecular modeling), and drug discovery technologies (e.g., structural biology). In so doing, we remained attentive to the consistency of emerging patterns across these dimensions as well as across the five projects. Table 3 gives an overview of data sources. ***INSERT TABLE 3 ABOUT HERE*** The primary source was 68 semi-structured interviews with scientists and senior managers directly involved in drug discovery.6 Our data comprises 24 interviews with project team leaders/senior managers, 28 with senior investigators (more experienced laboratory heads with Ph.D.’s), 12 with investigators (laboratory heads with Ph.D.’s), and four with research associates operating the lab equipment. We conducted 63 interviews at the informants’ work sites (in Europe and North America) and five by telephone.7 Interviews lasted between 60-90 minutes, and all but two were recorded and transcribed verbatim. For the two cases for which recording was not possible (at the informants’ request), we took notes and wrote detailed reports immediately afterwards (Miles & Huberman, 1994). We asked these informants to describe their everyday activities, responsibilities, project team workflow, interactions and interdependencies with other project team members, and the challenges they faced in drug discovery (appendix A shows the interview protocol). To keep the interviews close to the informants’ practice, we used probing questions (Miles & Huberman, 1994), asking for an in-depth account of recent experiences and events, not for reflections on vague concepts (Miller, Cardinal, & Glick, 1997). 6 7

Twelve informants were interviewed twice. An overview of the interview data and observational data is available from the authors upon request.


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14 Our data also included non-participant observations of 55 instances of interactions among project teams members during project team meetings where all specialist members—and occasionally other stakeholders, such as senior managers—of a drug discovery project unite, subteam meetings of selected project team members from various knowledge domains, and specialists’ informal discussions and everyday bench work within laboratory environments. Due to confidentiality, we could not record these meetings. We took extensive field notes on site to capture as much as possible of the conversation verbatim, and enriched these notes with further contextual information after the observation (Miles & Huberman, 1994). Moreover, we collected archival data to contextualize our interviews. Data included scientific publications, intranet entries, documents from the online project management platform, and minutes of meetings. Data Analysis The combined interview and observation data consisted of 930 pages of single-spaced interview transcripts (over 87 hours of interview recordings) and 167 pages of field notes from observations. We managed our data using NVivo 9, a software tool for qualitative data. Data analysis was done in three stages. First, to reconstruct project teams’ workflow of everyday activities and to disentangle how scientists coordinated work across knowledge domains, we initially engaged in open coding of raw interview data (Strauss & Corbin, 1998). To contextualize the interview data, we used field notes from observations and secondary data. Data analysis occurred in real time, starting when transcripts and field notes became available. Two authors coded the interview transcripts until the analysis stopped yielding sufficiently distinct first-order categories. Each first-order category was labeled consistently with informants’ terminology (e.g., first-order category “branching out into sub-teams” corresponds to interview excerpt “there is a scientific question and we need to sit down and talk about it. So why don’t we call a sub-team meeting to get those people that are relevant together and hear everybody’s

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15 input?”) (Gioia, Corley, & Hamilton, 2012). If coding labels conflicted, we discussed and crosschecked emerging codes to ensure that the data was particular to a given code and did not become decontextualized. Second, we used axial coding (Strauss & Corbin, 1998) to identify similarities and differences in the first-order categories, and we aggregated corresponding categories into second-order themes, giving them higher-order theoretical labels (e.g., “structuring around conjectural interdependencies”). As the research design is aimed at elaborating theory, we repeatedly consulted the coordination literature to help us interpret the findings in the light of prior work. In so doing, we aggregated second-order themes into higher-order theoretical dimensions (Gioia et al., 2012). This second stage produced a data structure with three aggregate theoretical dimensions. Figure 1 summarizes this structure. ***INSERT FIGURE 1 ABOUT HERE*** Third, we revisited the full data set in search of emerging patterns and relationships between the themes and theoretical dimensions. As we progressed towards a deeper understanding of these patterns and relationships, we developed a preliminary model of how knowledge creation was coordinated in the project teams. Finally, to lend increased credibility to our interpretations, we discussed our emerging model with several key informants within DrugCo (Miles & Huberman, 1994; Nag, Corley, & Gioia, 2007). FINDINGS Knowledge creation at DrugCo involves coordination of specialists’ work at the project team level and within emerging sub-teams. Following a brief description of a drug discovery project’s inception, we first explain the two-tier structure within which project team members assume different modes of specialist work, either as sub-team “insiders” or sub-team “outsiders,” each with its distinct responsibilities for contributing to the knowledge creation process. We next


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16 discuss three informal practices that enabled specialists within these sub-teams to manage interdependencies among their respective knowledge domains (i.e., informal coordination practices within sub-teams) and progress in knowledge creation. We then explain how specialists create formal sub-team structures within the confines of their project team and dynamically restructure these sub-teams according to conjectural and revealed interdependencies (i.e., formal structuring around interdependencies). In so doing, project teams accommodated the unpredictable and continuously changing need for contributions from different knowledge domains as the process unfolded. Table 4 provides detailed information on aggregate dimensions, second-order (emergent) themes and their definitions, and additional examples of first-order data. ***INSERT TABLE 4 ABOUT HERE*** Project Inception Drug discovery projects originate when senior scientists conduct individual laboratory experiments to identify a disease-associated gene, protein, or signaling pathway in cells (i.e., a target). In this inception phase, specialists investigate, for example, how the target behaves in the disease state. One project team leader in biology (project AutoPro) described the formation of a drug discovery project as follows: You have an idea for exploratory work to ensure the target is druggable and…that it is really important…. And then the complexity increases…you have to start working with other people, developing assays, running a screen, and setting up a flow chart. But the target validation does not really stop at a certain point because we always need to evaluate how good the target is in comparison to other targets. What are its advantages? Does this target have certain safety aspects? Is it better than other targets in efficacy? What are the indications that we want to focus on? You really have to develop a clear understanding of which disease you want to target.

This excerpt illustrates that when initial evidence looks promising, the individual scientist’s exploration develops into a more complex project—a team-based undertaking comprising specialists from complementary knowledge domains within the company. The initial individual exploration thus leads to core scientific questions, each of which foregrounds some missing scientific evidence on proposed cause-and-effect relationships between the molecular structure of

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17 compounds, the activity of the target, or the disease state in living organisms. These questions underlie the knowledge that scientists need to create for the project to progress towards clinical trials and the commercialization of a drug. The excerpt also shows that a compartmentalized approach to knowledge creation is insufficient for resolving core scientific questions: The expertise for creating knowledge around core scientific questions is situated within multiple knowledge domains. As a senior investigator in molecular modeling (project InflaPro) noted, “With ‘pure’ modeling we are not able to solve any big problems in drug discovery. We must carefully integrate our contributions with those from structural biology—the knowledge we have about structure-activity-relationships,8 chemical synthesis, and pharmacology.” Advances on core scientific questions thus require project teams to coordinate interdependencies among specialists from various knowledge domains. Specialist Work DrugCo uses a two-tier structure to coordinate the drug discovery process, with project teams forming the first tier. Project teams disaggregate the drug discovery process by delegating the responsibility for answering core scientific questions to a second tier of formally structured, selfmanaged sub-teams consisting of specialists from within the confines of the project team.9 As a senior investigator in structural biology (project InflaPro) notes, “The project team meeting is rather formal. Experts that don’t know particularly much about each other’s work come together [here]. These meetings are more about information exchange and discussing what to do next at a very high and strategic level.” Most in-depth day-to-day collaboration among specialists unfolds at the sub-team level. The advantage of the sub-team structure is that it significantly reduces the 8

Structure-activity-relationships (SARs) describe the causal linkages between the chemical structure of a drug-like molecule (“structure”) and its biological activity on the target (“activity”). 9 Each project team includes between three to five sub-teams at any time. Project teams on average include ca. 50 members, with about 20-30 members attending project team meetings (laboratory technicians do not attend). Subteams on average include 4-6 members (laboratory heads).


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18 perceived complexity of the process. As the same senior investigator adds, “There are so many smaller sub-teams where the actual work gets done…they do certain experiments I’m not aware of, I’m simply not on their mailing list. It would be too much information if I were included there as well.” A core feature of the sub-team structure is that it allows specialists to assume different responsibilities in the knowledge creation process. For each core scientific question, the mode of specialist work of a project team member is guided by whether the formal sub-team structure designates him or her as a sub-team insider (i.e., a member of a particular sub-team embedded within the larger project team) or a sub-team outsider (i.e., a member of the larger project team but not of that particular sub-team). Specialist work of sub-team insiders. The specialist work of sub-team insiders refers to project team members’ contributions to a sub-team to which they (formally) belong, and involves day-to-day knowledge creation activities and routine project team updates in project team meetings. Specialists’ isolated work (e.g., designing, modeling, conducting, and interpreting experiment results) is the primary source of input to knowledge creation within sub-teams. As a senior investigator in medicinal chemistry (project AutoPro) describes this work: No doubt…discussions [with the modeler] are of pivotal importance for medicinal chemists. Yet at one point…we have to give it a try. We have to go back in the lab, cook10, and test the compound. We have to generate results. After that, we can discuss things again. I think that it’s very important…that we don’t neglect the true chemistry activities. We still use most of our time to actually cook the compounds.

To integrate the findings from individual experiments conducted in different knowledge domains, specialists share their results with other sub-team members and jointly decide on further activities. A project team leader in biology (CanPro2) comments: “In sub-teams we really discuss issues in-depth. It’s not about overview or strategy…it’s about science and details.” A

10

Slang for “create a chemical reaction.”

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19 senior investigator in biochemistry (project CanPro2) points out that the sub-team structure offers efficient knowledge exchange among those specialists deemed necessary for resolving core scientific questions: I personally like these very focused meetings where only those people involved in that scientific question are there. [In sub-teams] we try to move things. We go straight to the point. The other day we had a meeting on protein purification. One hour later we knew exactly what the others were doing, they knew what we were doing, and we made some decisions for moving forward.

Specialist work of sub-team insiders also involves members’ responsibility for reporting their findings in monthly project team meetings. As a project team leader in medicinal chemistry (project AutoPro) explains, “project team meetings are about getting everybody on the same page and documenting progress. However, to be honest, the real work is not done in these meetings. It’s done on an everyday basis.” Updating allows the project team (both sub-team insiders and outsiders) to jointly discuss how to proceed with new results, and offers an opportunity for subteam insiders to receive potentially valuable input on their work. As one senior investigator in biology (project CanPro2) comments: In project team meetings all the different specializations meet again. Thus it could well happen that an outsider to your domain has a completely different idea. He looks at an experiment without having conducted it thousands of times before. In sub-teams we sometimes make decisions because we did so in the past. Having somebody from the outside increases the chances of seeing things differently, and it might result in cross-fertilizing. In the subteam we then deal again with solving the issue, given our specialization in that field.

Specialist work of sub-team outsiders. The specialist work of sub-team outsiders constitutes the contributions from project team members to a sub-team to which they do not belong. Although specialists outside of the sub-team do not formally share the sub-team’s responsibilities, they remain pivotal to knowledge creation by questioning sub-team members’ assumptions underlying experimental designs and stimulating them to consider new approaches. As a senior investigator in medicinal chemistry (project AutoPro) explains: Having an outside view typically helps. If you are “inside” you are frequently overwhelmed with details and do not see the forest for the trees. Sometimes, these questions from outside—which at first sight might appear naïve but sometimes really target the very core of the problem—are helpful. [Outsiders] might feel they don’t contribute something relevant, but these contributions are usually highly valued.


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20 The following excerpt from a team meeting of project AutoPro illustrates such an exchange. A member of the “hit list sub-team” 11 updated the project team on the idea of expanding screening efforts to new molecules. Sven, a structural biologist outside the sub-team, observed: Sven: “Maybe it doesn’t fit here, but I remember having worked on a propeller-like compound we once identified in our experiments but never followed up on. Don’t you think this could be an entry point for you guys?” Rob (project leader): “Nice new input, you [the sub-team] should definitively take Sven’s comment into consideration. I suggest that you meet in the sub-team again and come up with appropriate decisions.”

Such interventions by outsiders can prove valuable even in cases when they do not hold in-depth knowledge about the sub-team’s work. An investigator in medicinal chemistry (project CanPro2) explains: “There are [scientific questions] that are outside my field…. [But] even if it’s not your field, you have to ask questions, you can try to understand. Even a naïve question can trigger new thinking in a scientist who knows his field in-depth. There are never stupid questions.” As a senior investigator in medicinal chemistry (project InflaPro) points out, incorporating suggestions from sub-team outsiders is ultimately voluntary: “I think [an experienced biologist] would be able to suggest things to [the medicinal chemists]. It might well be that the chemists didn’t think about it. The chemists, however, then decide whether or not they would like to consider that suggestion and go forward with it.” Informal Coordination Practices Within Sub-Teams Within sub-teams, specialists need to manage their cross-domain interdependencies in day-to-day activities. Managing interdependence is paramount—yet inherently demanding—for answering core scientific questions assigned to the sub-team. As a senior investigator in medicinal chemistry (project AutoPro) explains: Clearly, most challenges we face are beyond pure chemistry. I would label [such problems] as true drug discovery activities…they are by far the most challenging ones. Consider, for example, how to optimize chemical compounds according to the data we get from pharmacokinetics, in-vivo activity, stability, or other assays. As soon as you improve one parameter, the others drop immediately. It’s like packing a suitcase that is already jam-

11

A “hit” is a compound showing significant activity on the disease target in the high-throughput screen, identified as “hitting” the target.

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21 packed: As we try to close one lock, the others for sure won’t be lockable anymore…and nobody knows why [laughter]. These are the main problems we face.

Our analysis identified three informal coordinating practices that specialists use to manage crossdomain interdependencies in sub-teams: anticipatory conforming, workflow synchronizing, and cross-domain triangulating. (See table 5 for examples.) While specialists performed diverse work (e.g., modeling and experimenting), used specialized jargon, and adhered to values and standards of excellence unique to their domains, these informal coordination practices were consistent across levels, domains, and projects. ***INSERT TABLE 5 ABOUT HERE*** Anticipatory conforming. Anticipatory conforming refers to specialists’ efforts to understand their interdependence with other knowledge domains in terms of the implications of their work for that of the specialists, and if necessary, compromise their domain-specific standards of excellence to meet cross-domain requirements. A senior investigator in high-throughput screening (project AutoPro) explains: We have to design our assays such that they support the medicinal chemists’ decision-making in the best possible ways. For instance, we first go for binding assays with the inactive enzymes. These offer the best possible starting point for finding binders of a particular class—even if we, the assay developers, would prefer working with the active enzyme from the beginning. The latter would be more interesting for us. Also, I always have to think about whether the hits we identify are interesting from a medicinal chemistry perspective. For instance, a compound might involve a very challenging synthesis. These thoughts are very important for moving on with the problem of finding new potential drug-like compounds.

The following excerpt exemplifies this comment. Two sub-team members of project InflaPro—Alex, a structural biologist, and Chris, a medicinal chemist—discussed which of two selection criteria Chris could use to reduce the hit list: the compound’s “IC50” values or the medicinal chemist’s more subjective assessment of “chemical attractiveness”: Alex: “I don’t get what chemical attractiveness means. That’s very fuzzy to me.” Chris: “Well, it’s indeed fuzzy. It’s basically the medicinal chemist’s gut feeling…hard to put in words or numbers. It’s whether or not we like a molecule—for a variety of reasons.” Alex: “Yes, for a variety of reasons, precisely. That’s hard to understand for non-chemists. I would go for IC50 to select the next round of compounds.” Chris: “Are you sure? I would go for chemical attractiveness.” Alex: “But why?”


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22 Chris: “We could go for IC50 as well. That’s very fine with me—anyway, from a chemistry point of view we would prefer IC50. It’s much easier to justify…. However, do you really want to crystallize yet another benzyl amide?” [overall laughter]12

This example illustrates specialists’ vigilance about the requirements and possible challenges of other sub-team members’ work. The medicinal chemist preferred selecting compounds based on the “IC50” criterion. However, anticipating that applying this criterion would complicate the work of the structural biologist (who was unfamiliar with that criterion), the medicinal chemist “relaxed” his domain-specific standards of excellence to allow an effective resolution of the subteam’s core scientific question (how to reduce the hit list). Conversely, when sub-team members are unaware of or disregard the requirements of other sub-team members, progress on core scientific questions is at risk. For example, in a sub-team meeting of project InflaPro, molecular biologist Adam presented the results of his analysis to structural biologist Mike and biochemist Roger (both relying on Adam’s findings): Adam: “You can say that the [amino acid X] corresponds to [type A] and [amino acid Y] corresponds to the [type B]. We could use that for differentiation.” Mike: “I’m not sure about that.” Roger: “No, Adam is right.” [Adam continues explaining how to differentiate between the two amino acids.] Mike: “You forget that this site is masked. We cannot use it.” Adam: “Ah, OK, I didn’t know that. I just looked at the sequence.” Roger: “So what can we conclude with regards to species differences? What species would you recommend for toxicity studies?” Adam: “That’s why you wanted to know about species differences. I don’t know. I didn’t look into that….” Roger: “So the conclusion is that we first have to know in depth the binding mode and then select the species.” [As Mike explains his requirements, Roger suggests ending this discussion.] Roger: “In the interest of time, could we do that offline? Because, besides you two, nobody can follow at the moment.” [overall laughter] Adam: “No, but it’s important. Now I’m starting to understand what you really want.”

During Adam’s updating, it became apparent that he did not know all cross-domain requirements when conducting his experiments—and thus could not contribute to the core scientific question.

12

Compounds belonging to the structural class of “benzyl amides” were known for being tough nuts to crack.

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23 Workflow synchronizing. Workflow synchronizing refers to specialists’ efforts to understand temporal interdependencies, and order and pace their work accordingly. A project team leader in medicinal chemistry (project CanPro3) explains: The pharmacologist tells me that he needs to grow some tumor in mice and that this will take him three weeks after injecting the cancer cells into the mouse. The tumor then starts to grow, and in three weeks the mice are ready for treatment with the compound. If you wait for more than three weeks, the mouse dies. So you have to have the compounds ready three weeks from now. That’s essentially what we discuss…whether I am ready with the compounds then.

Another project team leader in biology (project AutoPro) further explains that, to prioritize their work, specialists need to understand each other’s needs: When we do multi-disciplinary activities…people have like ten different things they need to achieve every day…and so what might be my priority may not be the priority for the colleague, and vice versa. That’s why…I think it’s always complex, but communication is a big one…just really ensuring that the right people have the right information and understand what is expected of them.

Cross-domain discussions on temporal interdependencies aid specialists in providing meaningful contributions to the sub-team. In the following excerpt (project InflaPro), in-vivo biologist Tim discusses his planning with fellow sub-team members: Tim: “I might start working on developing new biomarkers in mice now. I have some free resources in the lab.” Linda: “We don’t know yet whether we really need these biomarkers in mice or humans. We have to wait for others’ input here.” Ralph: “We need to act according to the project priorities and think about that at a later stage….”

By pointing out the temporal interdependence between Tim’s and other specialists’ work, his colleagues ensured the timeliness of Tim’s contribution by deferring his intended activities. Cross-domain triangulating. Given the precautionary principle in drug discovery, the value of knowledge created within the bounds of specialists’ knowledge domains derives from the degree to which specialists consider such knowledge reliable. In establishing the reliability of their contributions, specialists are dependent on other knowledge domains. Cross-domain triangulating refers to specialists’ efforts to establish the reliability of domain-specific findings. The following statement from a senior investigator in molecular modeling (project CanPro1) illustrates how the


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24 uncertainty in the characteristics of novel compounds and their biological effects necessitates such triangulation: In biochemical assays you have many artifacts…some of the compounds that we are screening are not pure, some are doing strange things to proteins. Especially when you don’t know anything about the target—when it is a really new target—you always wonder: Is this something real? The modeler—if he knows the pocket and its target very well—can really make a connection with the structure…because he can see what it is like in 3D and check on the computer whether your idea was right or not.

Specialists use cross-domain triangulating to scrutinize the findings and assumptions in their own work. In so doing, they either gain confidence that their output can feed into later stages of the process or become aware of inconsistent findings that necessitate more work. In describing an invitro experiment in a mouse model, a senior investigator in molecular biology (project InflaPro) explains how cross-triangulation involves close interaction between sub-team members: At the very beginning, it was really actually going back and forth…we had many discussions because when I come up with a question, [the X-ray specialist] looks into a sequence and comes back to me to make sure—when he starts to look into the structure—whether he understood the problem correctly, you know, that he has a structure in front of his eyes. Then we discussed…. We looked at the structure together and he explained to me…[and] also [asked me] what I think that this amino acid could be…. Of course at the end [the X-ray specialist] is the one solving the structure, and he is the expert in the field, but it was really going back and forth, and questions came iteratively.

Finally, triangulating findings across specialized domains involves sensitivity to the pragmatics of specialists’ language and dedicated efforts to resolve misunderstandings. As a project team leader in medicinal chemistry (project InflaPro) comments: Between chemists and biology we have to have the same platform of discussion, of language, just to be sure that they can exactly understand what I want. Sometimes, as chemists, we are using biological words in the wrong sense. For example, last time [the biologist] said this compound was “nicely absorbed,” and now she says that the compound was “badly absorbed.” For me it is nicely absorbed when it is metabolized. [She] said: “No, for me ‘nicely absorbed’ means that you retrieve the parent compound in the blood after some time, it does not mean that it is absorbed and then you have metabolism. That is not the same.” So you have this small discrepancy in language between specializations and have to be sure you are on the same page.

By engaging in a discussion on the domain-specific meaning of “nicely absorbed,” the chemist and the biologist could prevent that the chemist wrongly interpreted the biologist’s findings. As this excerpt shows, triangulating with sensitivity to the particulars of their counterpart’s knowledge domain is a key requirement for effective cross-disciplinary interactions.

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25 Formal Structuring Around Interdependencies New core scientific questions and progress on existing questions prompt project teams to continuously consider which combinations of knowledge domains are most optimal for moving the drug discovery process forward. Formal structuring around interdependencies refers to the process by which project team members collectively decide which of the specialized knowledge domains from within the project team’s confines are relevant and interdependent for resolving a particular core scientific question, and then structure ad hoc sub-teams by selecting and grouping specialists accordingly. Project teams thus continuously (re-)establish the grounds for substantive interactions in sub-teams. Structuring around conjectural interdependencies. Decisions to include specialists in subteams are primarily driven by project team members’ collective appraisal of the need for expertise with respect to specific core scientific questions. The following excerpt illustrates the formation of a sub-team in project CanPro1: Bill, a senior investigator in high-throughput screening, updated the project team (including project leaders Ann and Ryan) on his insights from a large screening of 1.5 million molecules, which resulted in the identification of roughly 25,000 compound hits. For practical use in future experiments, the hit list must be reduced to fewer than 5,000 compounds: Ann: “Should we start anticipating the criteria for restricting the hit list to 5,000 compounds now?” Ryan: “Yes, we need a sub-team here to come up with some criteria for decision-making.” Ann: “Ryan and I should probably think about who should be included….” Peter: “We need to have a chemist join that sub-team. Different chemists think about the same problem in different ways. Here we need different viewpoints.”

As the need for expertise is contingent on the largely unknown nature of novel scientific problems and unpredictable outcomes of sub-team specialists’ knowledge creation efforts, structuring around interdependencies among knowledge domains is essentially a conjectural


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26 undertaking—as specialists are well aware. Asked how cross-domain interactions emerge, a project team leader in medicinal chemistry (project AutoPro) explains: It is very important to realize that compared to other industries, where the process is really well defined, in our business this is not the case. I like to compare it with [a local newspaper] we visited a few years ago. There it was very obvious that [while] of course they don’t know what they will write [about] the next day…it’s very certain that the newspaper will be published the next morning, no matter what is in there…. So everything has its welldefined stages: At this time of the day you have to have this, and then it goes to press, and at night it gets distributed, and the next day you always have your newspaper. And for us it is basically: [In] the morning you come…you think you have a brilliant idea, you try to do an experiment, and in the evening or maybe days later you realize that this was not the right approach…you cannot plan for this. [Y]ou try the first approach, and realize that this is not the way it works, so you have to look for alternatives…. You ask around, you contact different people…. There’s no right answer, you get different opinions...and go in one direction or the other. So it’s not that one exactly knows what comes next.

As this excerpt shows, the uncertain nature of the drug discovery process imposes limits on specialists’ ability to predict which knowledge domains become relevant at what time. Structuring around conjectural interdependencies was a consistent pattern of activities whereby specialists across DrugCo’s project teams formally coordinate knowledge creation. Figure 2A shows how an initial conjecture about interdependencies among specialized knowledge domains relates to sub-team formation. In this example, a sub-team consisting of specialists in medicinal chemistry, biochemistry, and in-vivo biology is formed within the broader project team, which also includes specialists in molecular modeling, pharmacology, etc. ***INSERT FIGURE 2 ABOUT HERE*** Restructuring around revealed interdependencies. Sub-teams are formed with the intention of combining those specializations that can jointly create knowledge until a core scientific question is answered. In practice, however, specialists often need to restructure the initial subteam when new findings or obstacles reveal interdependencies with knowledge domains not yet represented. As a senior manager observes: “[S]ome of the team members become less important, some become more important, some that are less important now can become more important at a later stage, so there is a coming in and going out.” While specialists are mindful of the changing

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27 importance of knowledge domains throughout the drug discovery process, interdependencies among specialists arise unpredictably. For example, in the following excerpt from project CanPro1, the sub-team was using a rat model to study the potential interference of the target with a human metabolic reaction. While sub-team member Lucy, an in-vivo biologist, proposed an experimental design to the project team, Cam, a pre-clinical safety specialist outside her sub-team (but on the project team), commented: Cam: “Were rats really the best species to look at? Mice could be similar to humans.” Lucy: “We assumed rats to be similar to mice and thus to humans. I wouldn’t have expected any differences between rodents.” Cam: “Probably there is a difference. We could have a look at it, if you want us to.” Lucy: “Very good point. We definitely should test whether a rat responds similar to a mouse. Could you do that?”

Exercising their specialist work responsibilities in this way—Lucy as a sub-team insider and Cam as a sub-team outsider—thus revealed an unpredicted interdependence between their respective knowledge domains. To answer its core scientific question, Lucy’s sub-team now appeared to rely on Cam’s domain specific knowledge on how to test the potentially dissimilar response of rodents. This newly revealed interdependence triggered the restructuring of the sub-team by including Cam in Lucy’s sub-team after the meeting. *** INSERT TABLE 6 ABOUT HERE*** Table 6 shows examples of obstacles that were resolved through sub-team restructuring around revealed interdependencies, and implications of restructuring for knowledge creation. As the project CanPro3 example shows, unpredicted interdependencies even appeared during standard procedures. Figure 2B illustrates a restructured sub-team (compared to the initial structure in Figure 2A)—with molecular modeling now included but in-vivo biology excluded. Restructuring sub-teams during the project team meetings was critical for overcoming obstacles in drug discovery. The decision to restructure could be initiated and influenced both by


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28 sub-team insiders and sub-team outsiders, and depended on the extent to which project team members could recognize the problems with the current sub-team structure, and whether the members agree to move to change the structure. Yet the ability to restructure stood in constant tension with the potential hazard that sub-team members would tenaciously search for solutions within the safety of their current configuration. In the following excerpt (project CanPro1), medicinal chemist Helen and toxicologist Zoe update the project team on their sub-team’s difficulties with understanding why important compounds test positive on toxicity: Leon (project team leader): “So your recommendation would be to optimize the compounds based on other parameters and then get back to the [toxicity test] again?” Helen: “It’s really important data. If the compound really is positive here all my options are gone. I want to give it another try [to optimize the compound].” Zoe: “We [toxicology specialists] can take that up offline in a meeting with the chemists.” Paul (senior manager): “I don't think we should let the chemists further work on that. The risk is too high. This is too much.” Zoe: “I would really like to understand what makes these compounds become positive here. We have to understand that in more detail.”

Sub-team members Helen and Zoe’s suggestion to further modify the compounds and redoing the toxicity tests illustrates that relinquishing conjectural interdependencies often proves difficult. In such cases where uncertainty emerges from the sub-team’s findings without pointing to alternative courses of action, specialists tended to continue to model and experiment within existing structures. EMERGENT THEORETICAL MODEL The findings revealed distinct types of formal and informal coordination: formal structuring (of sub-teams) around interdependencies, and informal coordination practices within sub-teams (i.e., anticipatory conforming, workflow synchronizing, and cross-domain triangulating). Given these findings, we propose a model of how formal coordination structures and informal coordination practices interweave in multidisciplinary self-managed teams creating knowledge under unpredictable interdependence (see figure 3). First, we explain how the formal structure

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29 establishes a foundation for specialist contributions to knowledge creation and fosters the emergence of informal coordination practices that enable interdependent specialists to integrate their efforts. Second, we show how informal coordination practices can trigger changes in formal coordination structures. The model captures the process through which multidisciplinary project teams manage the continuously changing demand for expertise by dynamically restructuring those sub-teams to which the project team had delegated specific parts of the knowledge creation process. The Mutual Constitution of Formal Coordination Structures and Informal Coordination Practices From formal coordination structures to informal coordination practices. Formal structuring in self-managed multidisciplinary project teams involves the selection and grouping of project team members into sub-teams. This process is guided by project team members’ collectively held assumptions about interdependencies among their respective knowledge domains for knowledge creation. Two mechanisms underlie the link between the formal structure and the informal practices that specialists use to manage interdependence in multidisciplinary teams: (a) delegating responsibility for knowledge creation in relevant knowledge domains and (b) developing team members’ awareness of interdependencies. First, formal structuring allows project team members to decompose tasks and delegate responsibility for parts of the knowledge creation process to sub-team members. Project teams thus reduce the overall complexity of coordination among specialists from different knowledge domains. By associating membership in different categories of team structures with different responsibilities for knowledge creation (e.g., establishing the safety of a new compound), the formal structure conveys information about how project team members are expected to contribute (i.e., in terms of norms and behavioral patterns). This part of the formal structure functions as a


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30 cue for specialists to cognitively associate themselves as sub-team insiders or outsiders. Such self-categorization (McCall & Simmons, 1978; Stryker, 1980) prompts specialists to enact a corresponding mode of contribution that involves either actively creating in-depth domainspecific knowledge or employing their expertise in an outsider role (Turner, Oakes, Haslam, & McGarty, 1994). At DrugCo, specialists across the five project teams had an implicit yet broadly shared understanding that the specialist contributions expected from a sub-team insider (e.g., computational analysis and modeling) differed substantially from those expected from sub-team outsiders (e.g., questioning everyday work and suggesting new approaches). Once project team specialists joined a sub-team, their responsibilities changed to include a direct involvement in resolving core scientific questions. Second, in creating a formal structure that stipulates (sub-)team membership and defines the configuration of interdependent knowledge domains, specialists shape their shared mental representation of the web of interdependencies in which their work is embedded. Using the category of a sub-team, specialists develop a social awareness of how their contributions relate to those of other specialists in the collective endeavor (Tomasello, Carpenter, Call, Behne, & Moll, 2005), and infer which interactions to prioritize (Baumeister & Masicampo, 2010; Puranam et al., 2012). Developing awareness—albeit conjectural—of interdependencies is particularly relevant for coordinating knowledge creation in a multidisciplinary and uncertain context. It elevates the target of specialists’ efforts to the collective team level, thus mitigating the danger of specialists’ narrowly interpreting their knowledge creation responsibilities within their own domain. As specialists often engage in knowledge creation activities in isolation, without taking interdependencies into account, such a narrow domain-specific interpretation of responsibility is

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31 a common problem in multidisciplinary work, severely limiting the relevance of specialists’ contributions and hampering integration. Given these two mechanisms, the relationship between the formal structure and the emergence of informal coordination practices can be understood in terms of specialists’ efforts to manage conjectural interdependencies in a way that allows them to fulfill their responsibility for knowledge creation. Specialists are the primary individuals responsible for creating knowledge while under scrutiny by other team members whose needs and evaluation criteria they can only partly anticipate—due to both task uncertainty and lack of common ground between knowledge domains (Lerner & Tetlock, 1999). Specialists thus tend to exert themselves to develop a deep, accurate understanding of how they can contribute to the team’s overall tasks (e.g., De Dreu, Nijstad, & Van Knippenberg, 2008). As a result, they may intensify their engagement with other sub-team members to search for, disseminate, and integrate information (e.g., De Dreu et al., 2000). Our analysis shows that, to this end, sub-team members draw on the three informal coordination practices that we conceptualized as anticipatory conforming, workflow synchronizing, and cross-domain triangulating. From informal coordination practices to formal coordination structures. Two important features of informal coordination practices stand out. First, informal coordination practices help shape specialists’ work such that it contributes to the sub-team’s collective task. In so doing, informal coordination practices enable specialists to cope with the unpredictable nature of crossdomain interdependencies. Specifically, anticipatory conforming involves specialists’ vigilance in exercising their responsibility for knowledge creation by contemplating how applying domainspecific norms and behaviors for knowledge creation impacts the work of others. Such a vigilant approach compels seemingly interdependent specialists to specify the nature of their shared tasks and shape domain-specific knowledge creation efforts such that they contribute to achieving the


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32 team’s objective. Through workflow synchronizing, specialists create a mutual understanding of temporal interdependencies among team members. They can thus attend to their domain-specific responsibilities for knowledge creation in a timely manner and flexibly readjust the team’s overall workflow when new insights emerge. Cross-domain triangulating enables specialists to scrutinize the validity of their approach and the value of their findings across disciplinary boundaries. By seeking confirmation from other knowledge domains, specialists check whether their responsibility for contributing to the team’s collective effort is fulfilled. These informal coordination practices allow specialists to make their work more predictable across domains, thus facilitating the integration of their contributions with those of other team members in a vigilant, timely, and collectively validated way. Second, informal coordination practices encourage specialists to redesign formal coordination structures. The need for restructuring might arise from progress in knowledge creation leading to new scientific questions that prove difficult to answer within the current configuration of knowledge domains. Alternatively, specialists might encounter difficulties in producing predictive knowledge (Puranam et al., 2012) and fulfilling their responsibility for knowledge creation while using informal coordination practices within the context of the existing formal structure. In such cases, they may be unable to meet the needs of other specialists (Pfeffer & Sutton, 2000), attempt to provide contributions at inappropriate points in time, or repeatedly produce dissonant findings for unknown reasons. Experiencing such limitations of the formal structure encourages specialists to take a more skeptical attitude to initially conjectured interdependencies and collectively reconsider the relevance of knowledge domains beyond the confines of the present (sub-)team structure. Such a response can be explained by specialists’ need to reduce the fear of invalidity and avoid costly misapprehensions when the stakes involved in their activities are high (e.g., Mayseless and

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33 Kruglanski, 1987). In our case, running unnecessary or ill-timed experiments was very costly and could severely disrupt progress of the drug discovery project. Moreover, drawing false conclusions about the efficacy and safety of new compounds could significantly harm the public, the firm, and individual scientists (Aven, 2011). In DrugCo, sub-team specialists routinely reported their findings (or lack thereof) to the project team during monthly meetings. This allowed project team members from a wider set of knowledge domains to exercise their “outsider” responsibility to knowledge creation by posing questions to sub-team members and providing suggestions. In many instances we observed, the constructive comments and questions of project team members revealed interdependencies among knowledge domains spanning the sub-team’s boundaries. This process prompts specialists to formally transition into (and out of) sub-teams whenever doing so appears valuable for progressing in collective knowledge creation. In such a way, project teams dynamically activate specialists’ responsibility for knowledge creation and resolve incongruencies between the formal team structure and newly discovered interdependencies. This continuous structural adaptation is critical for coordinating specialists’ contributions in multidisciplinary teams when task uncertainty is so intense that no one can anticipate either the interdependencies or the outcomes. DISCUSSION Despite their potential for making radically new and valuable discoveries, multidisciplinary teams face significant coordination challenges involving the division of labor and integration of specialists’ efforts (e.g., Cronin & Weingart, 2007; Dougherty, 1992; Latour & Woolgar, 1986). Motivated by an increasing disconnect between design and practice-based perspectives on these “universal problems of organizing” (Puranam, Alexy, & Reitzig, 2014), this paper contributes integrative theory on how specialists in multidisciplinary teams coordinate knowledge creation in the face of unpredictable interdependencies. By simultaneously examining formal and informal


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34 coordination mechanisms, this study offers new insights on their mutual constitution, with implications for research on both organizational design and practice-based coordination. Implications for Research on Organizational Design Our study contributes to design-based theories of coordination and knowledge creation. Traditional contingency perspectives on organizational design emphasize the merits of creating team structures that match the team’s information-processing requirements arising from its task environment (e.g., Donaldson, 2001; Galbraith, 1977). An implicit assumption in research drawing on this structural contingency framework is that organizational designers choose team structures by accurately assessing interdependence among organizational members. Contrary to this assumption, our study shows that in the context of knowledge creation, organizational members may experience significant difficulties in designing optimal formal team structures around interdependencies. Specifically, our findings highlight the inability to understand the relevance of certain knowledge domains ex ante (i.e., during the initial design stage), and the problem of unforeseeable outcomes of the knowledge creation process. Our study thus suggest that theoretical accounts of how multidisciplinary teams coordinate knowledge creation cannot be reduced to explaining how designers of team structures optimally allocate tasks among specialists and integrate its members’ efforts within static systems of interdependencies. This study contributes to understanding how structural design unfolds under uncertain conditions that cause organizational members to face unknown and unpredictable interdependencies (e.g., Cardinal et al., 2011; Grandori & Soda, 2006; Miles, Snow, Mathews, Miles, & Coleman, 1997; Sherman & Keller, 2011). Our model proposes that the process of designing and adapting formal structures around conjectural and revealed interdependencies can result from specialists’ self-managed knowledge creation efforts. Within formally designed structures designed around conjectural interdependencies, specialists enact informal coordination

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35 practices, which not only help them to develop predictive knowledge, but also propel them to collectively search for and discover new interdependencies. To accommodate newly revealed interdependencies, project teams flexibly reallocate the responsibility for knowledge creation among project team members from different knowledge domains, thereby adjusting the (sub)team composition. Our theorizing about why this design dynamic occurs in a context of knowledge creation under uncertainty brings together the literatures on team design and team-level information processing rooted in lay epistemic theory (Kruglanski, 1989). In line with motivated information processing theory (De Dreu, et al., 2000), our findings show that bearing the responsibility for knowledge creation without possessing the necessary predictive knowledge to integrate their activities (Puranam et al., 2012) propels team members’ “willingness to expend effort to achieve a thorough, rich, and accurate understanding of the world” (De Dreu et al., 2008: 23). Furthermore, we show that such collective information processing efforts have important implications for overcoming obstacles in team knowledge creation (Schippers, Edmondson, & West, 2014) by increasing members’ understanding of not only the nature and intensity (Knudsen & Srikanth, 2014; Puranam & Raveendran, 2013; Sherman & Keller, 2011) but also the scope of latent interdependencies that shape formal structures. Our findings also enrich the literature on structural adaptation in teams. A longitudinal interpretation of arguments underlying structural contingency theory (Thompson, 1967) leaves open many issues about how structural designs adapt over time as task conditions change (Hollenbeck et al., 2011; Cronin, Weingart, & Todorova, 2011). The relatively few studies that explore the process and outcomes of structural adaptation conclude that appropriate coordination and communication behaviors are necessary for teams to adapt (Hollenbeck et al., 2011; Moon et al., 2004). Yet whereas prior empirical evidence on structural adaptation primarily concerns


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36 structures with fixed membership and relatively broad, overlapping knowledge domains (Hollenbeck, Beersma, & Schouten, 2012), our study offers in-depth insights into structural adaptation through ongoing flexible (re)configuration of highly specialized knowledge domains. This feature of our model displays close links to Bigley and Roberts’ (2001) findings from a study of fire departments. They found that, to adapt largely formalized organizational structure to complex and volatile task environments, designers tend to rely on variable structuring mechanisms, such as assigning roles and tasks to human resources, reassigning personnel to different positions, or deferring informal decision-making authority to more qualified individuals. However, in Bigley and Roberts’ study, designers of organizational structures could draw on reasonably well-defined tasks and interdependencies among diversely skilled individuals and so implement structural change in a top-down fashion. Our theoretical model complements and extends these findings in the context of cross-disciplinary knowledge creation by showing that structural adaptation does not necessitate an “omniscient designer” (Puranam & Raveendran, 2013) “inscribing” his or her intentions on a team by structuring it (Orlikowski, 2008). Nor does it require team members to hold an accurate representation of the optimal interdependencies that connect them (Puranam & Swamy, 2011). Instead, we propose that specialists’ conjectural, tentative representations of interdependence, in conjunction with their informal coordination practice, provide a basis on which structural team designs in highly uncertain contexts can evolve in the direction of fit (Cardinal et al., 2011; Siggelkow, 2002). Implications for Practice-Based Research on Coordination This study also contributes to practice-based research focusing on how coordination of expertise unfolds in situations with high complexity, rapid change, and uncertainty. This stream of research—drawing primarily on “high-reliability” or “fast response” organizations (e.g., Faraj & Xiao, 2006; Klein, Ziegert, Knight, & Xiao, 2006; Majchrzak, Jarvenpaa, & Hollingshead,

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37 2007; Weick & Roberts, 1993)—emphasizes the critical role of informal flexibility enhancing and improvisational coordination mechanisms. Our theoretical model confirms these observations and adds that to cope with the changing nature, intensity, and scope of interdependencies emerging in knowledge creation, team members practice coordination through a combination of anticipatory conforming, workflow synchronizing, and cross-domain triangulating. However, our model also differs from past work that primarily focused on how informal practices compensate for inertial forces of formal, hierarchical role structures (Klein et al., 2006) but that paid limited attention to the potential interaction between informal coordination practices and formal coordination structure (Okhuysen & Bechky, 2009). In contrast, we emphasize that informal coordination practices are inherently embedded within and circumscribed by formal structures, and demonstrate that formal structures provide critically important—albeit tentative— guidance for specialists to practice coordination in multidisciplinary knowledge-creating teams. By illuminating this relationship, our model fosters the integration of practice-based research with the literature on the enabling effects of formal structure (e.g., Adler & Borys, 1996; Bresman & Zellmer-Bruhn, 2013; Cardinal, 2001; Jelinek & Schoonhoven, 1990). Specifically, we propose that the need for flexibility and adaptability in multidisciplinary knowledge-creating teams arising from unpredictable interdependencies cannot be fully met by informal coordination practices (e.g., Bruns, 2013). Our model suggests that in complex multidisciplinary teams comprising specialized individuals facing highly uncertain tasks, knowledge creation relies on formal team and sub-team structures as a source of flexibility. Specialists draw on formal structures not only to make sense of their sub-team responsibilities but also to develop a vital awareness of the changing web of interdependence within which their knowledge creation efforts have a collective bearing.


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38 Without the ability to derive these cues from the formal team and sub-team structures, using coordination practices to develop cross-domain predictive knowledge and integrate specialists’ efforts would become highly time consuming and costly. The decomposition of teams into subteams around conjectural interdependencies among specialists greatly reduces the complexity of collective knowledge creation, and thus establishes the grounds for informal coordination practices. Sub-team structuring allows specialists to allocate attention to their specific responsibilities in knowledge creation and manage a narrower scope of conjectural interdependencies relevant for the uncertain tasks at hand. The model shows that such complexity reduction is an important condition for coordination practices to affect the managing of both current and emerging interdependencies. The coordination practices that we uncovered demand intense personal interaction in smaller groups if they are to effectively ensure everyday coordination among sub-team members, adapt coordination structures, and safeguard knowledge creation within higher-level organization structures. This feature of our model contributes a valuable understanding of how the dynamics of two-tier structuring interacts with coordination practices. On a related note, as practice scholars have often sought to uncover fallacies in the logic underlying the design perspective (Okhuysen & Bechky, 2009), they may have paid insufficient attention to the conditions under which coordination practices enable effective organizational structures. Notable exceptions include Brown and Duguid (1991), who proposed that effective organization structures should be designed around social practices wherever these practices are effective for solving organizational problems. Following a reverse logic, our theoretical model shows how coordination practices manifest the limitations of an existing configuration of knowledge domains and thus trigger structural adaptation. We therefore argue that to fully understand how coordination of knowledge creation across a team’s knowledge domains unfolds

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39 when interdependencies are unpredictable, managers and organization theorists need to consider the recursive effects between the team’s formal structure and the emergence of informal coordination practices. Boundary Conditions, Limitations, and Directions for Future Research Although our findings yield valuable insights into a phenomenon that has remained largely inaccessible to scholarly fieldwork, they should be interpreted in light of this study’s boundary conditions and limitations. Our study focuses on self-managed teams with the discretion and power to change their structures. Previous studies have suggested that the impact of structures can depend on the extent to which employees are involved in their formation and the degree to which these structures are aligned with their own goals (Adler & Borys, 1996; Langfred, 2007). Therefore, the autonomy of project team members in forming structural arrangements should be considered a relevant boundary condition to the relationship between informal coordination practices and the structural adaptations that our model reveals. Moreover, given the importance of the precautionary principle in drug discovery and scientists’ high awareness of the potential safety implications of mistakes, our research setting might reflect specific characteristics of members’ attitudes towards task uncertainty. Similarly, previous research has suggested that distinctive characteristics of the science-based context (e.g., high task uncertainty and complexity) are important contingencies in project design (Cardinal et al., 2011; Pavitt, 1999). Given the five- to six-year length of a typical drug discovery trajectory, our limited observation interval could not cover the complete trajectory of any one project. Although we were able to study coordination in the early to late stages of drug discovery, we are cautious about making claims about causal relationships between our findings and the ultimate success or failure of the drug discovery projects we studied. Future studies should examine the extent to


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40 which the structural adaptations and informal coordination practices we observed impact project performance. Moreover, as in any single-site study, the distinctive characteristics of the case company mean that further empirical research should examine to what extent our theoretical model is transferrable to different firms in the same industry or to varied sites in which multidisciplinary teams face similar coordination challenges. Another avenue for future research relates to our finding that as teams of specialists collaborate to create knowledge, interdependencies among knowledge domains change over time. This finding is important for the literature on the relationship between innovation performance and distance among specialist knowledge domains (e.g., Fleming, 2004; Kotha et al., 2013), because it implies that knowledge distance is a dynamic aspect of multidisciplinary teams. Further investigation of the evolution of knowledge distance within teams might thus add important new insights to the coordination literature. Practical Implications This study has three important practical implications. First, it indicates that a mix of coordination structures and practices are necessary for knowledge creation in multidisciplinary teams facing intense task uncertainty. Managers must provide sufficient autonomy and resources for such project teams to self-manage interdependencies. Specialists must have time and slack resources both to formally restructure the sub-teams and recruit specialists as needed, and to develop subtle, interpersonal coordination practices. Second, to manage cross-disciplinary interdependencies and enable vigilant, timely, and collectively validated integration of efforts, the specialists we studied sometimes had to forgo their domain specific standards of excellence for the sake of the team’s collective outcomes (cf. von Krogh, Haefliger, Spaeth, & Wallin, 2012). This suggests that performance appraisal in fundamental discovery needs to take a comprehensive view of specialists’ contributions to the team’s shared objectives. Importantly,

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41 managers should avoid the “appraisal trap,” whereby they evaluate individual effort and skill based on predefined standards within knowledge domains. Third, we found that sub-team outsiders—while not considered directly responsible for knowledge creation—contribute substantially to the work of sub-team insiders. Managers cannot force specialists to externalize their (tacit) knowledge (Osterloh & Frey, 2000), especially if these specialists do not feel responsible for a particular task. Instead, managers should encourage them to mutually support one another’s work and take an interest in the project’s overall progress through, for example, mentoring and training programs, project debriefings, and periodic social events. CONCLUSION This study examined how multidisciplinary teams coordinate knowledge creation while facing unpredictable interdependencies among knowledge domains. Our findings from early-stage drug discovery teams suggest a dynamic relationship between formal coordination structures and informal coordination practices. By continuously designing formal sub-team structures around conjectural interdependencies, project teams adaptively delegate responsibility for knowledge creation to relevant specialists. We show that formal structures not only reduce the complexity of collective knowledge creation at the project team level but also enable specialists in sub-teams to engage in informal coordination practices of anticipatory conforming, workflow synchronizing, and cross-domain triangulating. These informal coordination practices facilitate the integration of knowledge creation efforts and propel specialists to reveal new interdependencies that establish the ground for structural adaptation. Given the mutual constitution of these formal and informal coordination mechanisms in cross-disciplinary knowledge creation, this study underscores the necessity of further integrating organizational design and practice-based perspectives on coordination.


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42 FIGURES AND TABLES FIGURE 1 Overview of Data Structure

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43 FIGURE 2 Formal Structuring Around Interdependencies

FIGURE 3 A Model of the Mutual Constitution of Formal Structuring and Informal Coordination Practices


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44 TABLE 1 Examples of Knowledge Domains in Drug Discovery Project Teams Knowledge domain

Expertise

Examples of Activities

Examples of Artifacts and Tools

Medicinal chemistry

Designing and synthesizing molecular compounds with the aim of developing drug-like compounds

Finding ways of effectively synthesizing new compounds; designing new compounds with improved activity, stability, selectivity, or safety profiles

Chemical molecules; solvents; glassware (e.g., distilling column, clamps, boiling flasks, funnels); tongs; test tubes;

Molecular modeling

Running computational algorithms to fit Computationally designing new molecules to target proteins; docking fragments molecules binding specifically in the binding pocket of the target molecule; to the target molecule predicting the binding of molecules to target

In-vivo biology

Designing and conducting Injecting drug molecule in animal models; Tissue samples; animal models; colored animal experiments to link preparing and staining tissue sampled from pictures of tissues; in-vivo laboratories with target protein with disease state animal models; observing disease state of animals hutches of the model

Highthroughput screening (HTS)

Developing and operating HTS Scaling up assays; preparing samples for HTS assays to rapidly identify a large screening; maintaining screening infrastructure; number of potential drug-like interpreting HTS screens molecules (hits)

Producing proteins in bacteria or mammalian Developing biochemical assays cells; harvesting and purification of proteins; Biochemistry to assess the activity of target designing new assay formats (e.g., read-out of proteins assays); conducting the assays Molecular biology

Cloning and genetic engineering in bacteria or mammalian cells

Pre-clinical safety and toxicology

Conducting safety and toxicology experiments Assessing toxicology and safety with compounds; compiling safety profiles of profile of drug compounds molecules; anticipating patient population in clinical development

Computational infrastructure and hardware; 3D glasses; computational algorithms; software; three-dimensional rendering of molecules and target proteins; online databases

Screening infrastructure; well plates; pipetting robots; compound libraries; lists with activity profiles for over 10,000 compounds Proteins; bacteria; bio-reactors used for growing bacteria and mammalian cells; infrastructure to purify the proteins; Petri dish

Cloning genes in bacteria; generating “knockDNA; gene sequence; “laminar flow” out” animal model that miss specific genes; workstations; database; cells; bacteria; gene comparative analysis of genomes/gene sequences sequencer; DNA amplifier Lists and slides with safety profile of compounds; experimental set-up to conduct safety and toxicology experiments; data from clinical trials of benchmark compounds

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45 TABLE 2 Summary of the Five Project Teams Project Team

CanPro1

CanPro2

CanPro3

AutoPro

InflaPro

Novelty of the Target

Stage in Drug Discovery May 2011

Dec. 2012

Medium

Lead identification

Lead optimization

Very high

Target identification

Target identification

Therapeutic Area

Oncology

Oncology

High

Target validation

Terminated (by end 2012)

Oncology

High

Lead identification

Lead optimization

Autoimmune diseases

Very high

Assay development

Lead identification

Inflammatory diseases

No. of Research Sites

Exemplary Core Scientific Questions

1

Designing and synthesizing new compounds with optimized pharmacokinetics (i.e., stability in human body) and safety properties Establishing biological experiments in cells and in-vivo to understand efficacy of compounds in living organisms

3

Assessing the “druggability” of target (i.e., the potential to generate compounds that bind with high affinity to that target) Analyzing signaling pathway in cells where the target is involved

2

Designing novel approaches to identify new hits (i.e., molecular chemical molecules that modulate the activity of the target) Validating the roles that the target plays in the development of which disease states

1

Optimizing compounds with regards to activity and selectivity Identifying “new chemical matter” (i.e., new series of chemical molecules that are distinct with regards to their molecular structure)

1

Confining “hit lists” generated through high-throughput screening (i.e., what are the compounds identified as ‘hits’ the team wants to take any further?) Establishing an in-vivo model (i.e., animal model) linking the target with the disease molecule

Note: Stage in drug discovery follows Sams-Dodd’s (2005) classification: Target identification: identifying the disease-associated gene or gene product; Target validation: determining the therapeutic value of the target in the therapeutic indication; Assay development: expressing the target in biochemical or cellular assay systems; Lead identification: screening and selecting compounds for further optimization; Lead optimization: optimizing compounds of or target affinity and selectivity.


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46 TABLE 3 Data Sources Across Projects Project team Data Source

CanPro1 CanPro2 CanPro3 AutoPro InflaPro Other* Total Therapeutic indication specialists (e.g., pre-clinical safety, in-vivo biology)

Interviews

6

6

4

1

7

1

25

Chemistry specialists (e.g., molecular modeling, medicinal chemistry)

5

4

1

6

8

1

25

Drug discovery technology specialists (e.g., structural biology, high-throughput screening)

5

2

5

3

2

1

18

16

12

10

10

17

3

68

Project-team meetings

6

2

3

4

7

--

22

Sub-team meetings

5

2

2

2

5

--

16

Specialists’ informal discussions and everyday lab work

3

4

3

3

4

--

17

14

8

8

9

16

--

55

Subtotal

Observations

Subtotal Secondary data

Project team meeting slides; entries in project management database; research organization’s intranet; project team meeting minutes; scientific publications on disease background, target, and drug candidates.

* Interviews with project team leaders prior to the selection of the five drug discovery projects.

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47 TABLE 4 Aggregate Dimensions, Second-Order Themes, Definitions, and First-Order Categories Aggregate Dimension

Second-Order Theme

Definition

First-Order Categories With Examples of Interview Excerpts • Bench work in laboratory environment: “My contribution…concerns the strategy with respect to protein production, crystallization, selection of what compounds to be crystallized…. All of that has to do with my scientific expertise.” (CanPro1, senior investigator, structural biology) • Modeling and computational analysis: “I conduct computational investigation to predict a number of properties of compounds, such as solubility, stability, or degradation [in the human body]. That concerns modeling the interactions [between the compound and target] and also includes statistical analyses.” (InflaPro, senior investigator, structural biology) • Updating the project team on findings and problems: “…you try to update all the others [in the project team] on what is going on, which doesn’t mean there’s no discussion….” (InflaPro, project team leader, medicinal chemistry)

Specialist Work of Sub-Team Insiders

Project team members’ contributions to a sub-team to which they belong.

Specialist Work of Sub-Team Outsiders

• Questioning everyday work: “[In sub-teams] you are quite often doing things in the usual way. Sometimes it’s quite beneficial to get challenged and to rethink the processes.” (InflaPro, project team leader, medicinal chemistry) Project team members’ contributions to a sub-team • Suggesting new approaches: “No one [outside our specialization] will tell you: ‘Okay, you should make this to which they do not belong. type of modification because it will be super active [in the animal model].’ Rather, [these outsiders] will be there to help you to design or to guide you…. I think that’s very useful.” (InflaPro, project team leader, medicinal chemistry)

Specialist Work

(table continues on next page)


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48 TABLE 4 (CONTINUED) Aggregate Dimension

Informal Coordination Practices Within Sub-Teams

Second-Order Theme

Definition

Anticipatory Conforming

• Orienting domain-specific activities toward cross-domain contributions: “I consider myself a specialist in Specialists’ efforts to understand the crystallography with the goal of being a drug discovery scientist. My activities in crystallography should be implications of their work valuable to the discovery of new drugs; not the activities themselves should be at the center stage but how for other specialists, and if they contribute to answering scientific questions.” (InflaPro, senior investigator, molecular modeling) necessary, compromise • Compromising domain-specific standards of excellence to meet cross-domain requirements: “A modeler can their domain-specific have brilliant ideas, but if [medicinal chemists] are not synthesizing…it is useless. Sometimes the molecular standards of excellence to modeler designs the molecule in a way that is difficult to synthesize…. After a discussion with the medicinal meet cross-domain chemist or input from a chemist we arrive at the molecule which is really visible in terms of synthesis… you requirements. have to trigger this dialogue.” (CanPro1, senior investigator, molecular modeling)

Workflow Synchronizing

Cross-Domain Triangulating

Formal Structuring Around Interdependencies

First-Order Categories With Examples of Interview Excerpts

Specialists’ efforts to understand temporal interdependencies, and to order and pace their work accordingly.

• Understanding temporal interdependencies: “To develop the experiments I have to wait for the protein. I can’t do without. Similarly, the specialist who makes the protein…needs to know whether he is purifying the right thing, whether he loses activity.” (Senior manager, screening technology) • Ordering and pacing specialist contributions: “Normally, the technology guys take care of many projects in parallel. The structural biologist, for example, could work on four to five projects in parallel. But his input is very critical…. They just ask: ‘how long you can wait [for the results]? Two weeks? One month?’ I then say: ‘For us this is critical, but depends on you guys’ scheduling. At most we can wait one to two months.’ They then try to manage it somehow.” (CanPro2, senior investigator, medicinal chemistry)

Specialists’ efforts to establish the reliability of domain-specific findings.

• Aligning experimental conditions across specializations: “As I designed my experiments, I was sitting together with the others who designed [complementary experiments] before. We had to ensure that we aligned several parameters and used the same experimental conditions. For example, we had to make sure that we used the same substrate [for all experiments].” (CanPro3, research associate, screening technology) • Confirming domain-specific results: “I have seen many projects where in-vivo data [generated by biologists based on cells or living organism] was not fitting at all with in-vitro data [generated by biochemists based on biochemical experiments with isolated target]. In our case, I would say, for [one chemical series] we have a perfect match. In-vivo and in-vitro are just a perfect match. I think that is the first time in the past ten years I have seen that. It is very good.” (InflaPro, project team leader, medicinal chemistry)

Designing structural arrangements (i.e., subStructuring teams) that include Around knowledge domains Conjectural considered relevant and Interdependencies responsible for knowledge creation.

• Deciding which knowledge domains are relevant for answering core scientific questions: “We also had subteam meetings during the time we worked toward establishing the [new screening experiments]. To do so, we included two medicinal chemists, the biochemistry lab, and the structural biologist.” (InflaPro, project team leader, medicinal chemistry). • Branching out into sub-teams “There is a scientific question and we need to sit down and talk about it. So why don’t we call a sub-team meeting to get those people that are relevant together and hear everybody’s input?” (CanPro3, senior investigator, biochemistry)

Redesigning structural arrangements (i.e., subRestructuring teams) by including new Around Revealed knowledge domains or Interdependencies excluding irrelevant knowledge-domains.

• Including new knowledge domains: “Once we have the structure [of the target] and cannot explain it, we might also have to include [the structural biologist] in our sub-team. We then have to work together, coordinate efforts, and understand that piece of data.” (CanPro3, senior investigator, biochemistry) • Excluding irrelevant knowledge domains: “In the future, for example, biophysics may not be that important anymore. Now we understand how the molecules bind and we probably won’t go back to finding completely new compounds….” (Senior manager, medicinal chemistry)

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49 TABLE 5 Informal Coordinating Practices Within Sub-Teams: Interview Excerpts Anticipatory Conforming “Being a good medicinal chemist is simply not enough. It’s not sufficient to plan and cook interesting compounds. It requires much more involvement with the sub-team. Fitting my knowledge contribution to the other disciplines is precisely what makes this job so exciting.” (CanPro1, project team leader, medicinal chemistry) “We have to think about what a ‘good’ compound really means. A good compound actually is not the one looking nicely on my screen and being highly active. It’s one that still is active and medicinal chemists can actually synthesize.” (AutoPro, investigator, molecular modeling) “There is absolutely no point in modeling a compound when you know that the chemist won’t it take up. For instance, I worked in a team where the chemists were clear about not including any Alpha atoms in the molecules—even if it was quite popular at that time. So I of course did not include any Alpha atoms in the molecules I modeled. I have to accommodate to the preferences of others in the sub-team…they are also involved in answering that scientific question.” (InflaPro, senior investigator, molecular modeling) Workflow Synchronizing “We would like to study the pharmacological profile of some compounds. This implies that the pharmacologists should not be waiting with the animals. We have to ensure that chemistry produces enough compounds so that they can properly scale up production. Similarly, the protein production specialist must deliver in time to the crystallographer so that he can then start determining the three-dimensional shape of the target protein.” (InflaPro, senior investigator, molecular biology) “I talk to the crystallographers, and to the person doing nuclear magnetic resonance, so I know what they are expecting of me in the near future.” (CanPro2, research associate, structural biology) “We have a close interaction [with the assay development laboratory] because when we finish our compound the next step takes place in [this] laboratory.” (AutoPro, research associate, medicinal chemistry) Cross-Domain Triangulating “It would be a dream scenario if we would find a hit in our biochemical screen that is also independently found to be a hit in the cellular screens. That would more or less be the validation that the hit we have found also shows cellular activity. However, chances are pretty slim. We are talking about two experimental set-ups with different sensitivities. Of course …essentially we design the assays such that they should inform each other. In fact, the goal is to get hits coming from the cellular assays and to test them in the biochemical assay and vice versa.” (CanPro2, project team leader, biochemistry) “In case the team wants to know [how the drug molecule binds to the target], they send it to me. I perform the crystallization and might confirm that what the modeler and the chemist predicted was perfectly accurate. Yet there are also situations where this is not the case....” (CanPro3, investigator, structural biology) “The goal is to make the validation [of the target] scientifically more sound…. Over time, the goal is to gain confidence that what you are doing really has an effect at the end and delivers value to the patient.” (AutoPro, senior investigator, medicinal chemistry)


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50 TABLE 6 Examples of Restructuring Around Revealed Interdependencies Project team

Obstacle

InflaPro

Standard experiments did not result in A group of specialists in high-throughput identification of any new compounds: “Finding screening—who could run assays with new new compounds for [this target] is really read-out—was included in the sub-team. challenging.”

“[The screen] generated new chemical matter for the chemists to work on…and that was something really good because it’s usually a big effort to run a high-throughput screen and yes, they did it…it worked out.”

Fluorescent “tracer molecule” that tracks the location of the target protein in tissues did not show any activity: “One part [of the molecule] should bind to [the target] and the other part should be fluorescent. The molecule is one hundred percent pure, but it’s just not fluorescent. So what is happening there?”

A specialist in structural biology—who had the unique domain knowledge to investigate how the tracer molecule might mechanistically bind to the target based on three-dimensional models—was included in the sub-team.

“[The structural biologist] was like: ‘Oh, yeah the spacer is not long enough…there might be some interaction between the [target] and the fluorophore. Maybe the spacer should be bigger because on the other molecules the spacer is bigger.’ So now we redesigned the molecule with a bigger spacer and another way of connecting.”

Experiments to identify new active compounds yielded no results (i.e., the sub-team ran out of molecules that medicinal chemists could further optimize): “The difficult part now is to find potent molecules in a biochemical compound…it’s a really new target so you have to start from scratch. Usually it’s rather difficult to get compounds with high potency. However, this is necessary for having a chance of seeing some results in the following steps.”

A specialist in molecular modeling was added to the sub-team to run computational algorithms to identify complementary compounds.

Molecular modeling resulted in identifying new compounds—complementary to the ones identified based on biochemical assays: “[Molecular modeling] was the most powerful approach for developing the right molecules…the molecular modeler had the most important contribution here, I would say.”

Hit list with hundreds of compounds had to be reduced to have a workable set of compounds for optimization: “…the laboratory showed a hit list from that biochemical screen that included 776 compounds.”

A specialist in structural biology was included in the sub-team to check whether there could be complementary selection criteria applied for confining the number of compounds.

Different specializations included in the sub-team conducted complementary experiments to triangulate the structural biologist’s findings across knowledge domains: “It turned out there was a reactive site on the surface of the protein that was giving us a fair amount of background [i.e., false positives].”

(project team leader, biology) AutoPro (research associate, medicinal chemistry)

CanPro1 (senior investigator, molecular modeling)

CanPro2 (senior investigator, assay development)

CanPro3 (investigator, structural biology)

Restructuring Around Revealed Interdependencies

Purification of the target—a routine activity— could not be carried out with the typical protocols: “[The target] was very difficult in A specialist in protein purification was terms of purification…. Normally [purification] included in the sub-team to develop a new is very fast and we all did that during our Ph.D. protocol for effectively purifying the target. and postdoc time. We [i.e., the non-specialists] typically do everything on our own.”

Implications for Knowledge Creation

“As we needed a purified target for [further experimentation], it just happened that he was probably the best person to carry out this activity. He could make a high quality target protein very quickly.”

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60 APPENDIX A Interview Protocol

• •

Can you tell me about your work and contribution to [this project]? What does a typical workday look like for you? Which scientific questions do you try to answer in this project? o Follow up: Which scientific disciplines take part in this process? Which other scientific disciplines did you interact with until now to fulfill your work on this project? How do you expect that to develop? How do project team members possibly differ? People here at [DrugCo] talk a lot about “integrated drug discovery”: What does integration from your point of view imply? How does integration happen in [your project]? How are decisions made about who does what and when? Can you tell me about the different ways people collaborate in your project? o Follow up: How do they differ? How are collaborations established? What are your experiences in working in each of these forms of collaboration? How does it influence your work? What enables you to work efficiently with other disciplines? In collaborating with others, how do you develop a shared view of the work and tasks you will perform? How do you ensure that project team members “are in the know”? What information do you share with other team members? What material do you exchange with other disciplines when interacting? Tell me about a situation that shows how you exchange materials. How do you process and prepare information before sharing it with other team members? How do other project team members enrich what you know? To what extent do you need to understand other scientific disciplines to do your work? Can you tell me about the technologies you use in this project? How do these technologies relate with to workflow in the project? Can you guide me through an example of how you draw conclusions from data? Can you tell me about how you collaborate with others when drawing conclusions from data?

• •

Questions about project-teams and sub-teams when these were specifically mentioned as forms of collaboration: What feedback do people give each other in sub-team meetings, project-team meetings? How do you contribute to sub-teams meetings, project-teams meetings?

• • • • • • • •

• • • • • • • • •

Note: Core questions are listed. The interview protocol was adapted as we progressed in our data collection to ensure its alignment with emerging insights. As a result, different participants were asked somewhat different interview questions.

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61 Shiko M. Ben-Menahem ([email protected]) is a senior researcher and lecturer in the Department of Management, Technology, and Economics at ETH Zurich. He received his Ph.D. from Rotterdam School of Management, Erasmus University. His research interests include organizational and team design, coordination of knowledge intensive work, and temporal aspects of organizational adaptation and innovation. Georg von Krogh ([email protected]) is the Chaired Professor of Strategic Management and Innovation at ETH Zurich. He is also a member of the National Research Commission of the Swiss National Science Foundation. Georg has published widely on topics such as organizational knowledge creation and technological innovation. His current research focuses on the effectiveness and efficiency of different approaches to innovation within and between firms. Zeynep Erden ([email protected]) is a senior researcher and lecturer in the Department of Management, Technology, and Economics at ETH Zurich. She received her Ph.D. in management from ETH Zurich. Her research interests include creation and coordination of knowledge in teams, implementation of knowledge and innovation strategies in organizations, and their impact on firm performance, especially in the pharmaceutical industry. Andreas Schneider ([email protected]) is a business development manager at Ypsomed Group. He received his Ph.D. in management and organization theory from ETH Zurich. His research explores collaborative knowledge creation practices and approaches to new product development.

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