{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/140573"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/140573","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Knowledge Integration in Convergence Research: A Theoretical and Empirical Investigation","abstract":"Convergence research has gained traction as an approach to address complex societal and environmental challenges by integrating diverse forms of knowledge across disciplines and sectors. We know relatively little about how such integration is defined, structured, and enacted in practice. This dissertation examines how convergence is conceptualized and operationalized in a large, cross-disciplinary and cross-sectoral project, particularly in relation to knowledge integration (KI). It comprises of three interconnected studies focused on answering an overarching research question: How is convergence conceptualized and operationalized in a National Science Foundation Growing Convergence Research (NSF-GCR) project, particularly in relation to knowledge integration (KI)? Study 1 provided the theoretical and conceptual foundations for this research. It focused on four key objectives: (a) understanding the ontological and epistemological perspective on KI, (b) identifying the meta-theoretical and methodological approaches to KI, (c) identifying dimensions of KI in cross-disciplinary collaborations, and (d) developing a conceptual framework of KI to identify the types of knowledge assembled (inputs), how knowledge is exchanged and integrated (processes), and what knowledge integration yields (outputs). To do so, it combines a scoping review methodology with a cited reference search and synthesized three domains of literature: (1) studies of inter- and transdisciplinarity; (2) studies of knowledge co-production in sustainability research; and (3) studies focusing on factors influencing knowledge integration in the Science of Team Science (SciTS) field. The study identifies eight dimensions of knowledge integration: (1) types of knowledge integrated, (2) competencies and education required to practice knowledge integration, (3) organizational structure, (4) types of actor involvement, (5) stages of collaboration, (6) contextual factors, (7) processes and mechanisms of knowledge integration, and (8) types of knowledge integration outcomes. It further organizes these dimensions into a conceptual framework of KI using an Input-Process-Output (IPO) model by O'Rourke et al., (2016). This framework is intended to function as a heuristic to prompt teams to adapt it to specific contexts, projects, and team configurations. It can also be used as a scaffold for designing and evaluating knowledge integration efforts in diverse collaborative settings. The second and third studies used this conceptual and theoretical framework to understand how the members of a National Science Foundation-funded Growing Convergence Research (NSF-GCR) project understand and practice convergence research. Specifically, the second study examined the social architecture of the team Using Social Network Analysis (SNA) and self-reported measures of members' transdisciplinary orientation (Misra et al., 2015), this study uncovered the types of collaborative communities, broker, and integrator roles that emerged in the team and examined how these communities and roles corresponded to team members' network positions and orientations. The study modeled an undirected, weighted collaboration network using twenty team members' levels and frequencies of collaboration with peers and contextualized the network patterns with open-ended responses on team dynamics. It identified three collaborative communities: a leadership core of experienced integrators, mentor-mentee pairs, and domain anchors who provided technical expertise. Broker (major brokers, information carriers, satellite collaborators) and integrator (cross-cluster, hidden, within-cluster, narrow) role classification revealed that boundary spanning depended on the interplay of personal orientation, opportunity, and project context. Influence was distributed beyond formal leadership, boundary spanning was not determined by seniority, and subgroup expertise and between-group reach reinforced each other. Study 2 further outlined practical applications of SNA for the evaluation and design of scientific teams that aim for knowledge integration across disciplinary boundaries. Finally, Study 3 addressed how knowledge is assembled, translated, and routed across science and policy arenas the NSF-GCR project. Using an abductive research methodology, and participant interviews, story maps, and archival research as methods, this study: (a) understood how convergence is conceptualized and operationalized by academic and professional experts in the team; and (b) elaborated an earlier conceptual framework for knowledge integration elaborated in study 1 (Punjabi et al., 2025). I found that participants understood convergence as their ability to co-reason together under evolving constraints through adaptable representations, with progress marked not by consensus or cognitive reframing, but by artifacts that were routable, durable, and auditable across different venues and audiences. I also identified the conditions under which boundary objects become interpretable and actionable in environmental policy contexts. It also determined how boundary orchestration can yield legible and credible decision-making outputs and offered transferable insights and hypotheses to inform the design and evaluation of future convergence efforts tackling complex environmental challenges. Together, this three-manuscript dissertation offers a layered and evolving account of how convergence is conceptualized and operationalized in practice, especially in relation to KI. I examined KI from three vantage points: (a) as a plural, multidimensional construct in the literature; (b) as a patterned social architecture of roles, ties, and orientations in a convergence team; and (c) as boundary orchestration work through which people co-reason under evolving constraints via shared representations. Across these lenses, the dissertation suggests that convergence is not a single endpoint or a stable state of integrated knowledge, but a capacity of the team that develops and evolves over time.","abstract_html":"Convergence research has gained traction as an approach to address complex societal and environmental challenges by integrating diverse forms of knowledge across disciplines and sectors. We know relatively little about how such integration is defined, structured, and enacted in practice. This dissertation examines how convergence is conceptualized and operationalized in a large, cross-disciplinary and cross-sectoral project, particularly in relation to knowledge integration (KI). It comprises of three interconnected studies focused on answering an overarching research question: How is convergence conceptualized and operationalized in a National Science Foundation Growing Convergence Research (NSF-GCR) project, particularly in relation to knowledge integration (KI)? Study 1 provided the theoretical and conceptual foundations for this research. It focused on four key objectives: (a) understanding the ontological and epistemological perspective on KI, (b) identifying the meta-theoretical and methodological approaches to KI, (c) identifying dimensions of KI in cross-disciplinary collaborations, and (d) developing a conceptual framework of KI to identify the types of knowledge assembled (inputs), how knowledge is exchanged and integrated (processes), and what knowledge integration yields (outputs). To do so, it combines a scoping review methodology with a cited reference search and synthesized three domains of literature: (1) studies of inter- and transdisciplinarity; (2) studies of knowledge co-production in sustainability research; and (3) studies focusing on factors influencing knowledge integration in the Science of Team Science (SciTS) field. The study identifies eight dimensions of knowledge integration: (1) types of knowledge integrated, (2) competencies and education required to practice knowledge integration, (3) organizational structure, (4) types of actor involvement, (5) stages of collaboration, (6) contextual factors, (7) processes and mechanisms of knowledge integration, and (8) types of knowledge integration outcomes. It further organizes these dimensions into a conceptual framework of KI using an Input-Process-Output (IPO) model by O'Rourke et al., (2016). This framework is intended to function as a heuristic to prompt teams to adapt it to specific contexts, projects, and team configurations. It can also be used as a scaffold for designing and evaluating knowledge integration efforts in diverse collaborative settings. The second and third studies used this conceptual and theoretical framework to understand how the members of a National Science Foundation-funded Growing Convergence Research (NSF-GCR) project understand and practice convergence research. Specifically, the second study examined the social architecture of the team Using Social Network Analysis (SNA) and self-reported measures of members' transdisciplinary orientation (Misra et al., 2015), this study uncovered the types of collaborative communities, broker, and integrator roles that emerged in the team and examined how these communities and roles corresponded to team members' network positions and orientations. The study modeled an undirected, weighted collaboration network using twenty team members' levels and frequencies of collaboration with peers and contextualized the network patterns with open-ended responses on team dynamics. It identified three collaborative communities: a leadership core of experienced integrators, mentor-mentee pairs, and domain anchors who provided technical expertise. Broker (major brokers, information carriers, satellite collaborators) and integrator (cross-cluster, hidden, within-cluster, narrow) role classification revealed that boundary spanning depended on the interplay of personal orientation, opportunity, and project context. Influence was distributed beyond formal leadership, boundary spanning was not determined by seniority, and subgroup expertise and between-group reach reinforced each other. Study 2 further outlined practical applications of SNA for the evaluation and design of scientific teams that aim for knowledge integration across disciplinary boundaries. Finally, Study 3 addressed how knowledge is assembled, translated, and routed across science and policy arenas the NSF-GCR project. Using an abductive research methodology, and participant interviews, story maps, and archival research as methods, this study: (a) understood how convergence is conceptualized and operationalized by academic and professional experts in the team; and (b) elaborated an earlier conceptual framework for knowledge integration elaborated in study 1 (Punjabi et al., 2025). I found that participants understood convergence as their ability to co-reason together under evolving constraints through adaptable representations, with progress marked not by consensus or cognitive reframing, but by artifacts that were routable, durable, and auditable across different venues and audiences. I also identified the conditions under which boundary objects become interpretable and actionable in environmental policy contexts. It also determined how boundary orchestration can yield legible and credible decision-making outputs and offered transferable insights and hypotheses to inform the design and evaluation of future convergence efforts tackling complex environmental challenges. Together, this three-manuscript dissertation offers a layered and evolving account of how convergence is conceptualized and operationalized in practice, especially in relation to KI. I examined KI from three vantage points: (a) as a plural, multidimensional construct in the literature; (b) as a patterned social architecture of roles, ties, and orientations in a convergence team; and (c) as boundary orchestration work through which people co-reason under evolving constraints via shared representations. Across these lenses, the dissertation suggests that convergence is not a single endpoint or a stable state of integrated knowledge, but a capacity of the team that develops and evolves over time.","abstract_has_math":false,"creators":["Punjabi, Shruti Rajesh"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Planning, Governance, and Globalization","degree_department":"Public Administration/Public Affairs","school":null,"contributors":[],"advisors":[],"committee_chairs":["Misra, Shalini"],"committee_members":["Lim, Theodore Chao","Schenk, Todd Edward William","Galappaththi, Eranga"],"year":2026,"date_issued":"2026-01-02","date_published":"2026-01-02","updated_at":"2026-07-22T22:20:09Z","subjects":["Convergence Research","Knowledge Integration","Inter- and Transdisciplinary Research","Science of Team Science","Knowledge Co-production"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45525"],"render_values":[{"text":"vt_gsexam:45525","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/140573","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Misra, Shalini"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Lim, Theodore Chao","Schenk, Todd Edward William","Galappaththi, Eranga"]},{"key":"dc:contributor.department","label":"Department","values":["Public Administration/Public Affairs"]},{"key":"dc:creator","label":"Author","values":["Punjabi, Shruti Rajesh"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-01-03T09:00:19Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-01-03T09:00:19Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-01-02"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Planning, Governance, and Globalization"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Convergence Research","Knowledge Integration","Inter- and Transdisciplinary Research","Science of Team Science","Knowledge Co-production"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45525"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/140573"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Convergence research has gained traction as an approach to address complex societal and environmental challenges by integrating diverse forms of knowledge across disciplines and sectors. We know relatively little about how such integration is defined, structured, and enacted in practice. This dissertation examines how convergence is conceptualized and operationalized in a large, cross-disciplinary and cross-sectoral project, particularly in relation to knowledge integration (KI). It comprises of three interconnected studies focused on answering an overarching research question: How is convergence conceptualized and operationalized in a National Science Foundation Growing Convergence Research (NSF-GCR) project, particularly in relation to knowledge integration (KI)? Study 1 provided the theoretical and conceptual foundations for this research. It focused on four key objectives: (a) understanding the ontological and epistemological perspective on KI, (b) identifying the meta-theoretical and methodological approaches to KI, (c) identifying dimensions of KI in cross-disciplinary collaborations, and (d) developing a conceptual framework of KI to identify the types of knowledge assembled (inputs), how knowledge is exchanged and integrated (processes), and what knowledge integration yields (outputs). To do so, it combines a scoping review methodology with a cited reference search and synthesized three domains of literature: (1) studies of inter- and transdisciplinarity; (2) studies of knowledge co-production in sustainability research; and (3) studies focusing on factors influencing knowledge integration in the Science of Team Science (SciTS) field. The study identifies eight dimensions of knowledge integration: (1) types of knowledge integrated, (2) competencies and education required to practice knowledge integration, (3) organizational structure, (4) types of actor involvement, (5) stages of collaboration, (6) contextual factors, (7) processes and mechanisms of knowledge integration, and (8) types of knowledge integration outcomes. It further organizes these dimensions into a conceptual framework of KI using an Input-Process-Output (IPO) model by O'Rourke et al., (2016). This framework is intended to function as a heuristic to prompt teams to adapt it to specific contexts, projects, and team configurations. It can also be used as a scaffold for designing and evaluating knowledge integration efforts in diverse collaborative settings. The second and third studies used this conceptual and theoretical framework to understand how the members of a National Science Foundation-funded Growing Convergence Research (NSF-GCR) project understand and practice convergence research. Specifically, the second study examined the social architecture of the team Using Social Network Analysis (SNA) and self-reported measures of members' transdisciplinary orientation (Misra et al., 2015), this study uncovered the types of collaborative communities, broker, and integrator roles that emerged in the team and examined how these communities and roles corresponded to team members' network positions and orientations. The study modeled an undirected, weighted collaboration network using twenty team members' levels and frequencies of collaboration with peers and contextualized the network patterns with open-ended responses on team dynamics. It identified three collaborative communities: a leadership core of experienced integrators, mentor-mentee pairs, and domain anchors who provided technical expertise. Broker (major brokers, information carriers, satellite collaborators) and integrator (cross-cluster, hidden, within-cluster, narrow) role classification revealed that boundary spanning depended on the interplay of personal orientation, opportunity, and project context. Influence was distributed beyond formal leadership, boundary spanning was not determined by seniority, and subgroup expertise and between-group reach reinforced each other. Study 2 further outlined practical applications of SNA for the evaluation and design of scientific teams that aim for knowledge integration across disciplinary boundaries. Finally, Study 3 addressed how knowledge is assembled, translated, and routed across science and policy arenas the NSF-GCR project. Using an abductive research methodology, and participant interviews, story maps, and archival research as methods, this study: (a) understood how convergence is conceptualized and operationalized by academic and professional experts in the team; and (b) elaborated an earlier conceptual framework for knowledge integration elaborated in study 1 (Punjabi et al., 2025). I found that participants understood convergence as their ability to co-reason together under evolving constraints through adaptable representations, with progress marked not by consensus or cognitive reframing, but by artifacts that were routable, durable, and auditable across different venues and audiences. I also identified the conditions under which boundary objects become interpretable and actionable in environmental policy contexts. It also determined how boundary orchestration can yield legible and credible decision-making outputs and offered transferable insights and hypotheses to inform the design and evaluation of future convergence efforts tackling complex environmental challenges. Together, this three-manuscript dissertation offers a layered and evolving account of how convergence is conceptualized and operationalized in practice, especially in relation to KI. I examined KI from three vantage points: (a) as a plural, multidimensional construct in the literature; (b) as a patterned social architecture of roles, ties, and orientations in a convergence team; and (c) as boundary orchestration work through which people co-reason under evolving constraints via shared representations. Across these lenses, the dissertation suggests that convergence is not a single endpoint or a stable state of integrated knowledge, but a capacity of the team that develops and evolves over time."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Contemporary urban and environmental problems, like water pollution, deteriorating air quality, climate-related risks, and more cannot be solved by any single discipline, agency, or community working alone (NRC, 2014). Researchers from diverse fields, including engineering, social sciences, humanities, often work with policy makers and local stakeholders to address these challenges, yet we still know relatively little about how these collaborations bring different kinds of knowledge and expertise together in practice. Addressing these challenges requires convergence research, which involves collaborations among people from different fields and sectors to frame problems, share knowledge, and co-produce solutions (NSF, n.d.; Sharp et al., 2011). This dissertation focuses on knowledge integration (KI) - the process of combining scientific, professional, and experiential insights to develop shared understanding and coordinated action, as a way to understand what convergence research is and how it is practiced in large cross-disciplinary and cross-sectoral teams. It is guided by an overarching question: How is convergence conceptualized and operationalized in a National Science Foundation Growing Convergence Research (NSF-GCR) project, particularly in relation to knowledge integration (KI)? It examined this question through three interconnected studies involving theory-development and field-based inquiry, using the NSF-GCR project as a case. The NSF-GCR project was aimed at addressing the problem of inland freshwater salinization in the Occoquan watershed of Northern Virginia and involved social scientists, biophysical scientists from various academic institutions, and practitioners from different water-related organizations, transportation agencies, and NGOs in the region. The first study developed a theoretical and conceptual framework for knowledge integration using scholarly insights from three domains of literature: inter- and transdisciplinarity; knowledge co-production in sustainability research; and the Science of Team Sciences (SciTS) (e.g., Pohl et al., 2021; Stokols et al., 2008a; O'Rourke et al., 2016). It highlighted how scholars from different fields think about knowledge and identified what helps or hinders knowledge integration. The conceptual framework of KI explained how knowledge is synthesized at different stages of a collaboration and what types of KI can be produced. This framework was developed It further identified eight dimensions of KI: types of knowledge integrated, competencies and education required to practice knowledge integration, organizational structure, types of actor involvement, stages of collaboration, contextual factors, processes and mechanisms of knowledge integration, and types of knowledge integration outcomes. It structured these dimensions across four interconnected components of collaboration: knowledge gathering (inputs), structural dynamics and collaborative dynamics (processes), and integrative outcomes (outputs). This study showed that there is no single way to integrate knowledge and that KI depends on context, including which forms of knowledge are assembled, how are they valued, and how team structures, roles, and collaborative processes are aligned in a given project. The framework serves as a flexible and adaptive tool that teams, funders, and knowledge brokers can use to reflect on what counts as valuable knowledge in their context, how integration is attempted, and how progress might be recognized (Punjabi et al., 2025). The second and third studies used this conceptual framework to understand how different stakeholders in the NSF-GCR project understand and practice convergence research. Specifically, the second study focused on the structural roles that emerged in cross-disciplinary and cross-sectoral collaboration. It used Social Network Analysis (SNA) and self-reported measures of transdisciplinary orientation (Misra et al., 2015) to identify the three collaborative communities (experienced integrators, mentor-mentee pairs, and domain anchors); three types of brokers (major brokers, information carriers, satellite collaborators), and four types of integrator roles (cross-cluster, hidden, within-cluster, narrow). Together, these patterns showed that influence and integration did not rest only with formal leaders; junior and mid-career researchers often acted as \"hidden integrators\", and expertise was distributed across tightly knit subgroups linked by a small number of key bridges who helped assemble and integrate knowledge. This study suggested that large, cross-disciplinary and cross-sectoral teams would benefit from deliberately identifying these hidden integrators, recognizing their contributions, and using network mapping as a practical tool to monitor and evaluate how collaboration unfolds and who facilitates knowledge integration. Building on this structural understanding of cross-disciplinary teams, the third study examined how the team's everyday practices and shared tools supported or constrained knowledge integration in practice. Using insights from interviews with academic and professional experts and story maps they developed (team members) this study: (a) examined how convergence is understood and practiced by team members; and (b) elaborated on the conceptual framework for knowledge integration developed in the first study. I found that team members understood convergence as their ability to think through problems together under changing organizational, institutional, and regulatory constraints, using shared representations, such as jointly created figures and tables, that could be adapted and reused in different settings. For them, progress towards convergence was visible when project outputs could be trusted and used by both academic and policy-related audiences. The study also identified when these shared representations can support planning and policy work and how careful coordination of meetings, roles, and timelines can produce outputs that are both understood by and credible to academic and professional experts. It further elaborated on a set of mechanisms that facilitate KI and identified obstacles and risks that may constrain it if left unaddressed. Taken together, these three studies highlight that convergence is not an ideal end state, but a capacity that teams build and adjust over time as they learn to work together. This dissertation makes three key contributions. Conceptually, it advances what knowledge integration entails, and how convergence research is similar to or different from other collaborative approaches like multi-, inter-, or transdisciplinary research, or mode-2 knowledge production (Gibbons et al., 1994; Klein, 1990; Pohl and Hirsch Hadorn, 2007; Gajary et al., 2023; Misra et al., 2024). Empirically, it demonstrates how tools like scoping literature reviews, social network analysis, and qualitative methods like semi-structured interviews can be used to study cross-disciplinary and cross-sectoral collaborations. Practically, it provides a flexible, adaptive framework that team leaders, funders, and knowledge brokers can use to design, monitor, and evaluate convergence research efforts, ultimately improving collaborative efforts aimed at addressing complex urban and environmental challenges."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Doctor of Philosophy"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Knowledge Integration in Convergence Research: A Theoretical and Empirical Investigation"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Misra, Shalini"],"dc:contributor.committeemember":["Lim, Theodore Chao","Schenk, Todd Edward William","Galappaththi, Eranga"],"dc:contributor.department":["Public Administration/Public Affairs"],"dc:creator":["Punjabi, Shruti Rajesh"],"dc:date.accessioned":["2026-01-03T09:00:19Z"],"dc:date.available":["2026-01-03T09:00:19Z"],"dc:date.issued":["2026-01-02"],"dc:description.abstract":["Convergence research has gained traction as an approach to address complex societal and environmental challenges by integrating diverse forms of knowledge across disciplines and sectors. We know relatively little about how such integration is defined, structured, and enacted in practice. This dissertation examines how convergence is conceptualized and operationalized in a large, cross-disciplinary and cross-sectoral project, particularly in relation to knowledge integration (KI). It comprises of three interconnected studies focused on answering an overarching research question: How is convergence conceptualized and operationalized in a National Science Foundation Growing Convergence Research (NSF-GCR) project, particularly in relation to knowledge integration (KI)? Study 1 provided the theoretical and conceptual foundations for this research. It focused on four key objectives: (a) understanding the ontological and epistemological perspective on KI, (b) identifying the meta-theoretical and methodological approaches to KI, (c) identifying dimensions of KI in cross-disciplinary collaborations, and (d) developing a conceptual framework of KI to identify the types of knowledge assembled (inputs), how knowledge is exchanged and integrated (processes), and what knowledge integration yields (outputs). To do so, it combines a scoping review methodology with a cited reference search and synthesized three domains of literature: (1) studies of inter- and transdisciplinarity; (2) studies of knowledge co-production in sustainability research; and (3) studies focusing on factors influencing knowledge integration in the Science of Team Science (SciTS) field. The study identifies eight dimensions of knowledge integration: (1) types of knowledge integrated, (2) competencies and education required to practice knowledge integration, (3) organizational structure, (4) types of actor involvement, (5) stages of collaboration, (6) contextual factors, (7) processes and mechanisms of knowledge integration, and (8) types of knowledge integration outcomes. It further organizes these dimensions into a conceptual framework of KI using an Input-Process-Output (IPO) model by O'Rourke et al., (2016). This framework is intended to function as a heuristic to prompt teams to adapt it to specific contexts, projects, and team configurations. It can also be used as a scaffold for designing and evaluating knowledge integration efforts in diverse collaborative settings. The second and third studies used this conceptual and theoretical framework to understand how the members of a National Science Foundation-funded Growing Convergence Research (NSF-GCR) project understand and practice convergence research. Specifically, the second study examined the social architecture of the team Using Social Network Analysis (SNA) and self-reported measures of members' transdisciplinary orientation (Misra et al., 2015), this study uncovered the types of collaborative communities, broker, and integrator roles that emerged in the team and examined how these communities and roles corresponded to team members' network positions and orientations. The study modeled an undirected, weighted collaboration network using twenty team members' levels and frequencies of collaboration with peers and contextualized the network patterns with open-ended responses on team dynamics. It identified three collaborative communities: a leadership core of experienced integrators, mentor-mentee pairs, and domain anchors who provided technical expertise. Broker (major brokers, information carriers, satellite collaborators) and integrator (cross-cluster, hidden, within-cluster, narrow) role classification revealed that boundary spanning depended on the interplay of personal orientation, opportunity, and project context. Influence was distributed beyond formal leadership, boundary spanning was not determined by seniority, and subgroup expertise and between-group reach reinforced each other. Study 2 further outlined practical applications of SNA for the evaluation and design of scientific teams that aim for knowledge integration across disciplinary boundaries. Finally, Study 3 addressed how knowledge is assembled, translated, and routed across science and policy arenas the NSF-GCR project. Using an abductive research methodology, and participant interviews, story maps, and archival research as methods, this study: (a) understood how convergence is conceptualized and operationalized by academic and professional experts in the team; and (b) elaborated an earlier conceptual framework for knowledge integration elaborated in study 1 (Punjabi et al., 2025). I found that participants understood convergence as their ability to co-reason together under evolving constraints through adaptable representations, with progress marked not by consensus or cognitive reframing, but by artifacts that were routable, durable, and auditable across different venues and audiences. I also identified the conditions under which boundary objects become interpretable and actionable in environmental policy contexts. It also determined how boundary orchestration can yield legible and credible decision-making outputs and offered transferable insights and hypotheses to inform the design and evaluation of future convergence efforts tackling complex environmental challenges. Together, this three-manuscript dissertation offers a layered and evolving account of how convergence is conceptualized and operationalized in practice, especially in relation to KI. I examined KI from three vantage points: (a) as a plural, multidimensional construct in the literature; (b) as a patterned social architecture of roles, ties, and orientations in a convergence team; and (c) as boundary orchestration work through which people co-reason under evolving constraints via shared representations. Across these lenses, the dissertation suggests that convergence is not a single endpoint or a stable state of integrated knowledge, but a capacity of the team that develops and evolves over time."],"dc:description.abstractgeneral":["Contemporary urban and environmental problems, like water pollution, deteriorating air quality, climate-related risks, and more cannot be solved by any single discipline, agency, or community working alone (NRC, 2014). Researchers from diverse fields, including engineering, social sciences, humanities, often work with policy makers and local stakeholders to address these challenges, yet we still know relatively little about how these collaborations bring different kinds of knowledge and expertise together in practice. Addressing these challenges requires convergence research, which involves collaborations among people from different fields and sectors to frame problems, share knowledge, and co-produce solutions (NSF, n.d.; Sharp et al., 2011). This dissertation focuses on knowledge integration (KI) - the process of combining scientific, professional, and experiential insights to develop shared understanding and coordinated action, as a way to understand what convergence research is and how it is practiced in large cross-disciplinary and cross-sectoral teams. It is guided by an overarching question: How is convergence conceptualized and operationalized in a National Science Foundation Growing Convergence Research (NSF-GCR) project, particularly in relation to knowledge integration (KI)? It examined this question through three interconnected studies involving theory-development and field-based inquiry, using the NSF-GCR project as a case. The NSF-GCR project was aimed at addressing the problem of inland freshwater salinization in the Occoquan watershed of Northern Virginia and involved social scientists, biophysical scientists from various academic institutions, and practitioners from different water-related organizations, transportation agencies, and NGOs in the region. The first study developed a theoretical and conceptual framework for knowledge integration using scholarly insights from three domains of literature: inter- and transdisciplinarity; knowledge co-production in sustainability research; and the Science of Team Sciences (SciTS) (e.g., Pohl et al., 2021; Stokols et al., 2008a; O'Rourke et al., 2016). It highlighted how scholars from different fields think about knowledge and identified what helps or hinders knowledge integration. The conceptual framework of KI explained how knowledge is synthesized at different stages of a collaboration and what types of KI can be produced. This framework was developed It further identified eight dimensions of KI: types of knowledge integrated, competencies and education required to practice knowledge integration, organizational structure, types of actor involvement, stages of collaboration, contextual factors, processes and mechanisms of knowledge integration, and types of knowledge integration outcomes. It structured these dimensions across four interconnected components of collaboration: knowledge gathering (inputs), structural dynamics and collaborative dynamics (processes), and integrative outcomes (outputs). This study showed that there is no single way to integrate knowledge and that KI depends on context, including which forms of knowledge are assembled, how are they valued, and how team structures, roles, and collaborative processes are aligned in a given project. The framework serves as a flexible and adaptive tool that teams, funders, and knowledge brokers can use to reflect on what counts as valuable knowledge in their context, how integration is attempted, and how progress might be recognized (Punjabi et al., 2025). The second and third studies used this conceptual framework to understand how different stakeholders in the NSF-GCR project understand and practice convergence research. Specifically, the second study focused on the structural roles that emerged in cross-disciplinary and cross-sectoral collaboration. It used Social Network Analysis (SNA) and self-reported measures of transdisciplinary orientation (Misra et al., 2015) to identify the three collaborative communities (experienced integrators, mentor-mentee pairs, and domain anchors); three types of brokers (major brokers, information carriers, satellite collaborators), and four types of integrator roles (cross-cluster, hidden, within-cluster, narrow). Together, these patterns showed that influence and integration did not rest only with formal leaders; junior and mid-career researchers often acted as \"hidden integrators\", and expertise was distributed across tightly knit subgroups linked by a small number of key bridges who helped assemble and integrate knowledge. This study suggested that large, cross-disciplinary and cross-sectoral teams would benefit from deliberately identifying these hidden integrators, recognizing their contributions, and using network mapping as a practical tool to monitor and evaluate how collaboration unfolds and who facilitates knowledge integration. Building on this structural understanding of cross-disciplinary teams, the third study examined how the team's everyday practices and shared tools supported or constrained knowledge integration in practice. Using insights from interviews with academic and professional experts and story maps they developed (team members) this study: (a) examined how convergence is understood and practiced by team members; and (b) elaborated on the conceptual framework for knowledge integration developed in the first study. I found that team members understood convergence as their ability to think through problems together under changing organizational, institutional, and regulatory constraints, using shared representations, such as jointly created figures and tables, that could be adapted and reused in different settings. For them, progress towards convergence was visible when project outputs could be trusted and used by both academic and policy-related audiences. The study also identified when these shared representations can support planning and policy work and how careful coordination of meetings, roles, and timelines can produce outputs that are both understood by and credible to academic and professional experts. It further elaborated on a set of mechanisms that facilitate KI and identified obstacles and risks that may constrain it if left unaddressed. Taken together, these three studies highlight that convergence is not an ideal end state, but a capacity that teams build and adjust over time as they learn to work together. This dissertation makes three key contributions. Conceptually, it advances what knowledge integration entails, and how convergence research is similar to or different from other collaborative approaches like multi-, inter-, or transdisciplinary research, or mode-2 knowledge production (Gibbons et al., 1994; Klein, 1990; Pohl and Hirsch Hadorn, 2007; Gajary et al., 2023; Misra et al., 2024). Empirically, it demonstrates how tools like scoping literature reviews, social network analysis, and qualitative methods like semi-structured interviews can be used to study cross-disciplinary and cross-sectoral collaborations. Practically, it provides a flexible, adaptive framework that team leaders, funders, and knowledge brokers can use to design, monitor, and evaluate convergence research efforts, ultimately improving collaborative efforts aimed at addressing complex urban and environmental challenges."],"dc:description.degree":["Doctor of Philosophy"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:45525"],"dc:identifier.uri":["https://hdl.handle.net/10919/140573"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Convergence Research","Knowledge Integration","Inter- and Transdisciplinary Research","Science of Team Science","Knowledge Co-production"],"dc:title":["Knowledge Integration in Convergence Research: A Theoretical and Empirical Investigation"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Planning, Governance, and Globalization"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:20:09Z"}