<p>This research investigated the influence and relationship of two cognitive diversity frameworks on student design team knowledge integration capabilities and team contribution among seventy-five (75) student teams in Purdue’s Tech 120: Design Thinking in Technology course. </p> <p> </p> <p>When in cognitively diverse teams, students do not effectively integrate the knowledge available to them. Past research results in this area have further demonstrated that students tend to get worse at collaboration as the cognitive differences emerge and are exposed over time. The costs of this lack of collaboration and assimilation of knowledge assets are significant, such as diminished creativity, coordination, and other team performance measures. The purpose of this study then, was to provide student design teams with models or frameworks for visualizing and understanding the cognitive diversity available to them in their team and test the impact these frameworks have on various measures of team effectiveness: knowledge integration, psychological safety, and individual contribution. </p> <p> </p> <p>Cognitive diversity frameworks in question have been used successfully in various industry and organizational settings. The first, is the FourSight Thinking Profile™. This framework is used to understand one’s creative problem-solving preferences and how those preferences (high, neutral, and low) impact group dynamics. The second, is the AEM-Cube®. This framework draws on several theoretical foundations to assess an individua’s patterns of thinking and responses to change. Both the FourSight Thinking Profile™ and the AEM-Cube® have shown to help teams in industry settings collaborate (DeCusatis, 2008; Reynolds & Lewis, 2017), but their use in educational settings to solve the knowledge integration and team contribution problem in student teams is untested. </p> <p> </p> <p>The nearly 470 students in Purdue’s TECH 120 course were organized into teams ranging from 3-5 members by their instructors, thus creating a total of 129 teams. The researcher then divided the 129 teams into two fairly equal treatment groups. Each treatment group was given one of two cognitive diversity assessments (FourSight or AEM-Cube) to complete individually, time to review the results, and then asked to create a team charter or contract where students discussed cognitive strengths and weaknesses and how they planned to manage those assets and deficiencies as they worked on a 4-week long design thinking project. Only 75 teams completed all steps of the treatment (either FourSight or AEM-Cube) and thus were the focus of analysis. </p> <p> </p> <p>The major conclusions of this study are that while neither the FourSight or AEM-Cube frameworks for cognitive diversity were more effective in raising student knowledge integration capability or overall team contribution, these frameworks did not negatively impact the student experience; high levels of psychological safety were maintained among both more homogeneous teams and those that were more heterogeneous; and higher levels of knowledge integration capabilities and team contribution were achieved by students in varying degrees of diversity of creative problem-solving preferences and strategic agility. While the reason(s) for such high scores for knowledge integration capability, team member contribution, and psychological safety are unknown, the students reported that the processes by which these teams integrated their knowledge assets and solicited the contribution of their team members was both positive and effective. </p> <p> </p> <p>Further research into the effectiveness of the treatment, the influence of demographic diversities on team functions, and the experience of the 54 student teams that did not complete the treatment are needed to elucidate and understand the findings of this study. </p>

This multi-article dissertation explores the potential for the undergraduate peer mentoring experience to contribute to transformative learning for those who serve in peer mentoring or peer leadership roles. While past research has established that peer mentors experience a variety of positive outcomes associated with their leadership experience, there are gaps in the literature with regard to how the peer mentor experience can be intentionally designed as a high-impact practice for student leaders. Through three qualitative studies, as well as a comprehensive literature review, this dissertation addresses this gap by exploring what peer mentors report learning through their mentoring experiences (Article #1), the types of experiences that contribute to transformative learning (Articles #2 and #3), and how transformative learning can be facilitated through a particular designed training intervention (Article #4). Based on the findings of these four studies, an emerging framework is proposed to describe the characteristics of high-impact and transformative PM learning environments. This framework suggests that transformative PM learning is most likely when (a) the learning of PMs themselves is an explicit objective of the program or initiative, (b) PMs are engaged in purposeful routines and structured practices that facilitate learning, (c) PMs are exposed to unfamiliar and challenging situations in their mentoring practice, (d) theoretical and conceptual understandings (e.g., selected readings) are integrated into PM development; (e) PMs are provided with frequent and structured opportunities for reflection on their experiences, and (f) PMs are part of a supportive mentoring community. Recommendations for the design of high-quality PM learning environments are made based on the elements of this framework. Additionally, directions for future research on peer mentoring as a high-impact practice are made.

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在持續創新的需求下，跨公司合作已成為企業整合應用知識，創新求變的重要策略。本文融合知識基礎觀點和組織學習觀點，指出跨公司合作所帶來的組織創新績效，係因合作夥伴知識整合的結果。因此研究的目的除了要實證知識整合與組織創新績效的關係外，主要在發展一個理論架構，說明影響跨公司合作創新的因素，及這些因素之間的關係。在研究設計上，本文係以我國高科技產業、資訊服務業及金融服務業做為問卷調查的對象，將所收集的資料透過探索性分析與驗證性分析兩階段檢定本文所提出之量測模式與研究假說，結果得到下列結論： 1. 跨公司合作為企業所帶來的創新績效，可以用知識整合的觀點來解釋，當企業知識整合能力越好，跨公司合作創新績效會越好。 2. 知識整合能力受到企業知識吸收能力的影響，吸收能力係企業先前知識的蓄積量，以及應用這些知識的能力之綜合。 3. 知識整合能力亦受到企業與夥伴合作的能力之影響，合作能力包括企業與夥伴建立信任關係，以及溝通、協調的能力。 4. 知識特質受知識整合機制的中介而影響創新績效，跨公司合作所帶來組織的創新績效，主要不受合作所牽涉知識之外顯╱內隱與複雜性特質的影響，必須應知識特質的不同而採取適當的整合機制後，才能因知識整合而產生創新績效。 5. 吸收能力、合作能力與知識整合機制是相互影響的因素，三者形成企業知識整合創新能力的主要構面，是企業追求跨公司合作創新必須重視的要素。 / Inter-firm collaboration has been an important strategy for firms to integrate and apply knowledge among their partners in order to obtain innovation performance under uncertainty circumstances. This study combining the knowledge based view and the organizational learning perspective, considers the innovation performance improvement as the result of knowledge integration. Besides examining the relation between innovation performance and knowledge integration, the purpose of this study is to find the factors influencing inter-firm collaboration performance and the relationships among these factors. The data collecting from Taiwan’s high-tech, information service and finance industry were used in the exploratory and confirmatory phases to evaluate the instruments and test the hypotheses. The conclusions are listed as below: 1. The innovation performance gaining from inter-firm collaboration can be considered as the result of knowledge integration. While the knowledge integration capability of a firm is better, the firm’s innovation performance in inter-firm collaborations will be better too. 2. The knowledge integration capability of a firm is influenced by the absorptive capacity of the firm. And the firm’s absorptive capacity derives from the firm’s prior knowledge accumulation and the capability to apply their knowledge. 3. The knowledge integration capability of a firm is also influenced by their cooperative competency. The cooperative competency is composed of three interrelated facets: trust, communication and coordination. 4. Firm’s innovation performance in an inter-firm collaboration doesn’t directly influenced by the explicit/tacit and complexity characteristics of the knowledge which appeared in the collaboration. To produce innovation performance, knowledge with different characteristics should be integrated by suitable integration mechanisms. 5. Absorptive capacity, corporative competency and knowledge integration mechanism construct firm’s “knowledge integration and innovation capability”, which is important to firm’s innovation performance in inter-firm collaborations.

隨著知識經濟時代的來臨，掌握知識可幫助組織提昇其競爭力，因此對於知識的產生、儲存、應用和整合，已成為熱烈討論的議題，本研究針對知識整合議題進行探討；而在知識呈現方式中，決策樹（Decision Tree）形式知識為樹狀結構，可以用圖形化的方式來呈現，它的結構簡單且易於瞭解，本研究針對決策樹形式知識來探討其知識整合的課題。 本研究首先提出一個合併選擇決策樹方法MODT（Merging Optional Decision Tree），主要是在原始決策樹結構中增加一個選擇連結（Option Link），來結合具有相同祖先（Ancestor）的兩個子樹；而結合方式是以兩兩合併的方式，由上而下比對兩棵決策樹的節點（Node），利用接枝（Grafting）技術來結合兩棵樹的知識。再者利用強態法則（Strong Pattern Rule）概念來提昇合併樹的預測能力。 其次，由於MODT方法在合併兩棵不同根節點的決策樹時，會形成環狀連結的情形而破壞了原有的樹形結構，以及新增的選擇連結會增加儲存空間且不易維護，因此本研究提出決策樹合併修剪方法DTBMPA（Decision-Tree-Based Merging-Pruning Approach）方法，來改善MODT方法的問題，並且增加修剪程序來簡化合併樹。此方法包括三個主要程序：決策樹合併、合併樹修剪和決策樹驗證。其做法是先將兩棵原始樹經由合併程序結合成一棵合併樹，再透過修剪程序產生修剪樹，最後由驗證程序來評估修剪樹的準確度。本研究提出的DTBMPA方法藉由合併程序來擴大樹的知識，再利用修剪程序來取得更精簡的合併樹。 本研究利用實際信用卡客戶的信用資料來進行驗證。在MODT方法的實驗上，合併樹的準確度同時大於或等於兩棵原始樹的比例為79.5%；並且針對兩者的準確度進行統計檢定，我們發現合併樹的準確度是有顯著大於原始樹。而在DTBMPA方法的實驗中，合併樹的準確度優於原始一棵樹的比率有90%，而修剪樹的準確度大於或等於合併樹的比率有80%。在統計檢定中，合併樹和修剪樹的準確度優於一棵樹的準確度達顯著差異。且修剪樹的節點數較合併樹的節點數平均減少約15%。綜合上述，本研究所提之MODT方法和DTBMPA方法皆能使得合併樹的準確度優於一棵樹的準確度，而其中DTBMPA方法可以取得更精簡的合併樹。 此外，就決策樹形式知識整合的應用而言，本研究提出一個決策樹形式知識發掘預測系統架構，其主要的目在於提供一個Web-Based的知識發掘預測系統，以輔助企業進行知識學習、知識儲存、知識整合、知識流通和知識應用等知識管理的功能。期能藉由使用這套系統來發掘企業內部隱含的重要知識，並運用此發掘的知識進行分類和預測工作。它包含三個主要子系統，即知識學習子系統、合併決策樹子系統和線上預測子系統，其中合併決策樹子系統就是應用本研究所提出之決策樹形式知識整合方法來進行知識整合處理。 有關後續研究方面，可針對下列議題進行研究： 一、就決策樹形式知識整合架構中，探討決策樹形式知識清理單元，即前置處理部份的功能設計，期能讓合併樹結合有一定質量的決策樹形式知識。 二、就綜合多個預測值部份，可加入模糊邏輯理論，處理判定結果值之灰色地帶，以提昇合併樹的預測準確度。 三、就決策樹本身而言，可進一步探討結合選取多個屬性來進行往下分群的決策樹。針對分類性屬性的分支數目不同或可能值不同時的合併處理方法；以及數值性屬性選取不同的分割點時的合併處理方法。 四、探討分類性屬性的分支數目不同或可能值不同時之合併處理方法，以及數值性屬性選取不同的分割點時之合併處理方法。 五、對於合併樹的修剪方法，可考量利用額外修剪例子集來進行修剪的處理方法，並比較不同修剪法之修剪效果及準確度評估。 六、探討多次合併修剪後的決策樹之重整課題，期能藉由調整樹形結構來提昇其使用時的運作效率，且期能讓合併樹順應環境變化而進行其知識調整，並進一步觀察合併樹的樹形結構之變化情形。 七、就實際應用而言，可與廠商合作來建置決策樹形式知識發掘預測系統，配合該廠商的產業特性及業務需求來設計此系統，並導入此系統於企業內部的營運，期能藉此累積該企業的知識且輔助管理者決策的制定。 / In the knowledge economy era, mastering knowledge can improve organization competitive abilities. Therefore, knowledge creation, retention, application, and integration are becoming the hottest themes for discussion nowadays. Our research focuses on the discussion of knowledge integration and related subjects. Decision trees are one of the most common methods of knowledge representation. They show knowledge structure in a tree-shaped graph. Decision trees are simple and easily understood; thus we focus on decision-tree-based knowledge in connection with the theme of knowledge integration. First, this research proposes a method called MODT (Merging Optional Decision Tree), which merges two knowledge trees at once and adds an optional link to merge nodes which have the same ancestor. In MODT, we compare the corresponding nodes of two trees using the top-down traversal method. When their nodes are the same, we recount the number of samples and recalculate the degree of purity. When their nodes are not the same, we add the node of the second tree and its descendants to the first tree by the grafting technique. This yields a completely merged decision tree. The Strong Pattern Rule is used to strengthen the forecast accuracy during the merged decision trees. Secondly, when we use the MODT method to merge two trees which have different roots, the merged tree has cyclic link in the root. It makes the merged tree not a tree structure, so we propose another approach called DTBMPA (Decision-Tree-Based Merging-Pruning Approach) to solve this problem. There are three steps in this approach. In the merging step, the first step, two primitive decision trees are merged as a merged tree to enlarge the knowledge of primitive trees. In the pruning step, the second step, the merged tree from the first step is pruned as a pruned tree to cut off the bias branches of the merged tree. In the validating step, the last step, the performance of the pruned tree from the second step is validated. We took real credit card user data as our sample data. In the MODT experiments, the merged trees showed a 79.5% chance of being equal or more accurate than the primitive trees. This research result supports our proposition that the merged decision tree method could achieve a better outcome with regard to knowledge integration and accumulation. In the DTBMPA simulation experiments, the percentage accuracy for the merged tree will have 90% of chance that is greater than or equal to the accuracy for those primitive trees, and the percentage accuracy for the pruned tree will have 80% of chance that is greater than or equal to the accuracy for merged tree. And we also find that the average number of nodes of the pruned tree will have 15% less than that of the merged tree. Eventually, our MODT and DTBMPA methods can improve the accuracy of the merged tree, and the DTBMPA method can produced smaller merged tree. Finally, in respect to the application of the Decision-Tree-Based Knowledge Integration, this research proposes an on-line Decision-Tree-Based knowledge discovery and predictive system architecture. It can aid businesses to discover their knowledge, store their knowledge, integrate their knowledge, and apply their knowledge to make decisions. It contains three components, including knowledge learning system, decision-tree merging system, and on-line predictive system. And we use the DTBMPA method to design the decision-tree merging system. Future directions of research are as follows. 1.Discussing the Decision-Tree preprocessing process in our Decision-Tree-Based Knowledge Integration Architecture. 2.Using the fuzzy theory to improve the accuracy of the merged tree when combining multiple predictions. 3.Discussing the merge of the complicated decision trees which are model trees, linear decision trees, oblique decision trees, regression trees, or fuzzy trees. 4.Discussing the process to merge two trees which have different possible values of non-numeric attributes or have different cut points of numeric attributes. 5.Comparing the performance of other pruning methods with ours. 6.Discussing the reconstruction of the merged trees after merging many new trees, discussing the adaptation of the merged trees to the changing environment, and observation of the evolution of the merged trees which are produced in different time stamp 7.Implementation of the on-line Decision-Tree-Based knowledge discovery in a real business environment.

Project leadership requires a diverse blend of technical and behavioral skills. Researchers have focused on the technical aspects of project management, leaving a void in understanding the behavioral skills of project leadership. The purpose of this correlational study was to gain insights into the behavioral aspects of projects by understanding the social capital and knowledge integration abilities of project leaders. Nahapiet and Ghoshal's social capital definition and its structural, relational, and cognitive attributes form the basis for the social capital theory constructs used in this study. The focus of the research questions was on the relationship of social capital to knowledge integration and project success. A self-designed survey (α = .925) was used to measure the latent variables of a project leader's social capital and knowledge integration abilities on the observed variable of project success. Survey research, conducted using a sample of project management professionals (N = 108), elicited project members' perceptions on the behavioral aspects of project leaders. Structural equation modeling validated that knowledge integration assists in achieving project success and that 2 types of social capital, structural and relational, have a significant influence on knowledge integration. Structural social capital has a positive effect, and relational social capital has a negative effect. The findings indicated that project management professionals need not only technical skills, but also behavioral skills. Having project leaders with the right blend of competencies will improve project success rates, affecting social change by enabling organizations to achieve greater economic benefits from better understanding the behavioral aspects of project teams.

Peter Drucker於1993 年在其著作中指出，在新的經濟體系裡，知識並不是和人力、資本或土地等並列為製造資源之一，而是唯一有意義的一項資源，在一個以知識為基礎的社會裡，最根本的經濟資源，不再是資本或自然資源，也不再是勞力，取而代之的是知識。既然知識是最重要的一項資產，企業就應該花更多的時間來管理知識，才能為企業帶來創新並創造價值。因此企業如何有效的創造和應用知識，就成為經營管理的主要手段（Soo & Park,1999）。而知識也逐漸被視為一種策略性的資產，知識的取得、整合、儲存、分享和應用為企業建立競爭優勢的必備能力（Zack 1999）。 Grant (1996)進一步提出知識基礎理論，他認為動態競爭環境的成功的要訣，就是將組織能力視為一種知識整合之方法，並且認為知識是組織最重要的策略性資源。不管是任何產品，無形或是有形的產品其背後的基礎都是知識智慧的累積，因此以知識為基礎的創新，是決定企業成功的關鍵因素。 本研究嘗試從知識基礎論的角度出發來探討在不同的創新類型之間，應採用何種的知識流通模式與知識整合機制較容易成功，研究方法採用質性研究之個案訪談法，研究對象為知識密集產業，總共訪談了八位高階主管，並將之整理成四個個案，經由分析歸納後得到以下幾點結論。 一、技術創新所需的知識屬性偏向於專屬性高；管理創新所需的知識屬性偏向於內隱性低。 二、知識屬性會影響創新過程中的知識取得與流通模式。當知識屬性為路徑相依性高時，組織的知識與流通傾向於採用內部自行發展；當知識屬性為路徑相依性低時，組織的知識與流通傾向於採用外部知識來源。 三、知識的取得與流通模式並不會對創新類型造成顯著影響，這是因為知識的取得與流通若要對企業創新造成進一步的影響，尚須配合組織本身的吸收與整合機制以及組織內部知識創造的過程，才能有效的進行企業創新。 四、創新類型與知識屬性會影響創新過程中的知識吸收與整合。當創新類型為技術創新，知識屬性為內隱性高、專屬性高時，組織的知識吸收與整合機制傾向於採用多方面的模式，包括了個人知識整合、團隊交流與文件化；當創新類型為管理創新，知識屬性為內隱性低時，組織的知識吸收與整合機制傾向於採用單方面的模式，而且多為團隊交流。 五、企業所擁有的互補性資產越高越有利於創新，從個案中發現企業若擁有良好的互補性資產，則在取得知識後，越能快速的進行企業創新。 六、不同的創新為企業所帶來的效益也不同，研究發現技術創新（產品創新與製程創新）為企業所帶來的效益通常是局部的；而管理創新（組織創新與策略創新）為企業所帶來的效益通常是全面的。 / Peter Drucker refer to knowledge, not compare with manufacturing resource such as labor or capital or land, is the only meaningful resource in new economy system in his writings in 1993.The fundamental economic resource in knowledge based society is not capital or natural resource or labor but knowledge. The enterprise should spend more time to manage knowledge since knowledge is the most important assets. Thus knowledge could bring innovation and create value for enterprise. Therefore, how to create and apply knowledge effectively has become a major issue for business administration（Soo & Park,1999）. The knowledge has been regard as a strategic asset gradually. The acquisition, integration, accumulation and diffusion of knowledge is a necessary capability of enterprise to build competitive advantage. Grant refer to knowledge based theory further in 1996.He consider the successful method in dynamic competitive environment is to regard organization capability as a method of knowledge integration and knowledge is the most important strategic resource of organization. The fundamental of all product, visible or invisible, is the accumulation of wisdom. Therefore, the knowledge-based innovation is the critical point of enterprise’s success. This thesis try to discuss what kind of knowledge-diffusion model and knowledge-integration model would easily success in different innovation type from knowledge-based theory. The research methodology is case study of quality method and research target is knowledge intensive industry. The research has interviewed eight managers and concluded four case. Finally, we have some conclusion below : 1. The knowledge of technical innovation is partial to high specificity； the knowledge of managerial innovation is partial to low tacit. 2. The character of knowledge would influence knowledge acquisition and flow model. When character of knowledge is partial to high path dependent, the knowledge acquisition and flow model is partial to develop by enterprise itself；When character of knowledge is partial to low path dependent, the knowledge acquisition and flow model is partial to acquire from outside. 3. The knowledge acquisition and flow model would not influence innovation type obviously. If knowledge acquisition and flow model would influence innovation type obviously, it should match up with organization’s absorptive and integrative mechanism. 4. The innovation type and knowledge character would influence knowledge absorption and integration during innovation process. When innovation type is partial to technical innovation and knowledge character is partial to high tacit and high specificity, then organization’s absorption and integrative mechanism will be partial to adopt all-around methods, include personal knowledge integration, organizational communication and documentlization. When innovation type is partial to managerial innovation and knowledge character is partial to low tacit, then organization’s absorption and integrative mechanism will be partial to adopt one-way method, such as organizational communication. 5. The more complementary assets enterprise have, the easier enterprise to innovate. If enterprise have higher complementary assets, then they could proceed innovation immediately after acquire knowledge. 6. The different types of innovation bring different benefit to enterprise. We find technical innovation（product innovation and process innovation）bring partial benefit to enterprise, and managerial innovation （organizational innovation and strategic innovation）bring all-around benefit to enterprise.

The global business environment has changed dramatically in recent years, as competition in complex knowledge-based economies has increased. Enterprise Resource Planning (ERP) systems have been viewed as a way to manage increased business complexity, leading to the rapid adoption and implementation of such systems, as ERP can support enterprises to improve their competitiveness. Knowledge management (KM) is crucial for ERP systems implementation, however a highly demanding task. Therefore, the primary concern of this research is to examine the effectiveness of knowledge management activities that would contribute to achieve ERP implementation success. This study adopted mixed methods approach by combining semi-structured interviews and a questionnaire to collect empirical data from ERP professionals in both manufacturing and service sector organisations. In the qualitative phase, it develops the “framework of integrative knowledge” based on empirical evidence, that can improve KM competence for ERP implementation success. Data analysis has been undertaken using a combination of thematic analysis and comparative analysis with respect to 14 ERP implementations in the UK. The framework integrates multiple perspectives in terms of knowledge components to enhance KM competence, including knowledge types, knowledge layers, KM lifecycle and knowledge determinants. It discovered 19 knowledge determinants to drive knowledge management activities during ERP projects, which is another vital contribution to the existing knowledge. Furthermore, the study develops the “knowledge network model” for ERP implementations in order to facilitate the knowledge flows between various stakeholders involved in ERP implementations, which can help to understand the interactions between the knowledge components. Moreover, sub-knowledge types (knowledge elements) under each knowledge type were discovered through empirical evidence. The quantitative phase was adopted to extend the findings of the qualitative phase. The knowledge types and knowledge elements were prioritised using Analytic Hierarchy Process (AHP) method through an online AHP based questionnaire with 77 responses from ERP professionals involved in UK ERP implementations. Furthermore, knowledge prioritisation demonstrates how effectively the framework of integrative knowledge can be used during ERP implementations with the help of prioritised knowledge. In total 4 knowledge types and 21 knowledge elements were ranked based on their contribution to achieve ERP success; four variables of information quality, systems quality, individual impact and organisational impact were used to measure ERP success. This study has number of theoretical contributions including framework of integrative knowledge, knowledge network model for ERP implementations and ERP knowledge prioritisation. Moreover, the framework of integrative knowledge can provide ERP practitioners with useful guidance on what the key knowledge determinants are and how the relationships between knowledge components should be best managed to achieve ERP implementation success in business reality.

Current research on knowledge integration offers valuable structural analyses of factors that influence knowledge integration, performance outcomes, and knowledge integration mechanisms. Less attention has been paid to how knowledge integration is carried out over time in cross-functional development projects. This thesis is based on a year-long field study of an Information Systems Development Project. The study shows how the knowledge integration process was repeatedly interrupted by different problems that could not be resolved by merely relying on integration mechanisms that were imposed by the top management. Instead, a bottom-up dynamic evolved where the project members and participating project managers managed to reestablish coordination and knowledge integration through the invention of different ‘collective heuristics’. A novel model of Dynamic Knowledge Integration is presented which claims that knowledge integration contains two interplaying processes; one consisting of different knowledge integration mechanisms and activities, and one consisting of the collective heuristics that were invented and employed when unexpected problems emerged. In general, this research argues that knowledge integration can be understood as a dynamic process, of which both knowledge integration mechanisms and collective heuristics constitute core elements. / Aktuell forskning inom området kunskapsintegration erbjuder värdefulla strukturella analyser av påverkansfaktorer, prestationsutfall och mekanismer för kunskapsintegration. Mindre uppmärksamhet har riktats mot att förstå hur kunskapsintegration åstadkoms över tid i tvärfunktionella utvecklingsprojekt. Avhandlingen bygger på en ett år lång fältstudie av ett informationssystemutvecklingsprojekt. Studien visade att kunskapsintegrationsprocessen ideligen avbröts av olika problem som inte kunde lösas med de integrationsmekanismer som den högsta ledningen infört. Istället utvecklades en ”underifrån-dynamik” där projektmedlemmarna och de deltagande projektledarna lyckades återställa koordinering och kunskapsintegration genom skapandet av olika ”kollektiva heuristiker”. En ny modell för Dynamisk Kunskapsintegration presenteras som visar att kunskapsintegration inrymmer två samverkande processer; en som består av olika mekanismer och aktiviteter för kunskapsintegration och en som består av de kollektiva heuristikerna som uppfanns och användes när oväntade problem uppstod. Mer generellt visar denna forskning hur kunskapsintegration kan förstås som en process i vilken mekanismer och heuristiker utgör centrala element som båda behövs för att förklara processens dynamiska karaktär.

<p>In recent years, science has shifted from a focus on reductionist explanations of biological phenomena to a more integrated, systems approach. This shift has made its way into curricular recommendations for undergraduate education. To understand complex biological phenomena, it has been argued that students employ mechanistic reasoning, in which one describes a mechanism by identifying the activities that produce change, the entities which engage in activities, and the starting and ending conditions. Students’ use of mechanistic reasoning requires engaging in the complex task of simultaneously integrating and coordinating multiple elements across space and time. In addition, students must link and organize their scientific ideas and then structure their thoughts into a network of knowledge, as described by the theory of knowledge integration. Previous studies that have explored students’ understanding of scientific concepts using knowledge integration as a lens found that students’ nonmechanistic ideas prevented them from identifying gaps in the connections between their ideas and from forming normative knowledge. Thus, this dissertation investigates the features of undergraduate biology students’ systems thinking using knowledge integration and mechanistic reasoning as conceptual and analytical frameworks. Using a semi-structured interview, we asked students to describe functional definitions of and relationships between three fundamental modules in biology (i.e., gene regulation, cell-cell communication, and the relationship between genotype and phenotype). In the first study, we found that the majority of students did not have normative functional definitions for how and why gene regulation occurs or how phenotype is regulated. When describing the relationships in an open context, most students expressed unidirectional, linear knowledge networks which lacked Mechanistic connections. In our second study, we examined how students described a transition point in biofilm development after being cued to think about the three modules. Though students struggled to transfer over relevant ideas to the biofilm context (such as gene regulation and cell-cell communication processes), we found that explanations were more specified in the nature of connections and content including more Mechanistic descriptions. In the third study, we explored features of biology students’ and instructors’ knowledge networks in an open context and situated to a context of the participants’ choice. Within an open context, most students described multidirectional, non-linear knowledge networks similar to instructors. In the specific context, the majority of students described non-linear knowledge networks, but some students modified their structures to be linear. Although the structures became less complex in the specific context, the nature of connections and content became more specified. Across all studies, we found that context greatly affected students’ systems thinking as revealed by the changing features of the knowledge networks. Specifically, context helped students identify what relationships they deeply understood and could transfer and allowed for the creation of a detailed explanation relevant to the specific biological phenomenon. For students to develop a broad systems perspective of biology, we recommend instructors engage students in the process of knowledge integration. Embed opportunities for students to think about biology concepts in various contexts, particularly where students grapple with nuanced and complex transfer of ideas. These practices will encourage students to form causal, mechanistic linkages between concepts and build an integrated, expert-like understanding of biology.</p>