1 Scientific decision support for decision makers in practice through collaborative knowledge management - A systemic approach and a case study integrated flood management - Thesis submitted in partial fulfilment of the requirements of the degree Doctor rer. nat. of the Faculty of Forest and Environmental Sciences, Albert-Ludwigs-Universität Freiburg im Breisgau, Germany by Tuck Fatt Siew Freiburg im Breisgau, Germany 2009
2 Name of Dean: Prof. Dr. Heinz Rennenberg Name of Supervisor: Prof. Dr. Dr. h.c. Gerhard Oesten Name of 2 nd Reviewer: Prof. em. Christian Leibundgut Date of thesis defence:
3 i Preface I was, at the beginning of my PhD study, involved in the EU INTERREG IIIB project WaReLa (Water Retention by Land-Use). The transnational project aimed at developing a transnational decision support instrument for spatial planning to decrease flood disasters, which are caused by small and medium-sized rivers, by precautionary land-use in meso-scale catchment areas. As one of the 11 project partners, the Institute of Forestry Economics contributed to the development of an Eco-Efficiency Analysis (EEA) concept. This concept was complementary to the physical process-based decision support systems. The final output was called EEA-DSS (Eco-Efficient Decision Support Systems). This decision support tool should serve the purpose of assisting spatial planners or decision makers in evaluating the impacts and feasibility of potential water retention measures to be implemented in forestry, agriculture and urban sectors. The project has ended but the tool has not been implemented successfully in practice. Reflecting on this experience, a series of questions came across my mind. I asked: Why did it fail?, What was the reason?, How does decision support operate in reality? Bearing these questions in mind, I started to look for the answers through intensive literature research and analysis. I found out that WaReLa is just one of the many examples of normative decision support projects, which produce decision support instruments to objectively support decision-making processes. The normative way of decision support exerts explicitly or implicitly how decision support instruments should operate in practice according to the norms, values, and assumptions of the researchers. From my point of view, decision support researchers lack of understanding about how the prevailing decision support practices in reality are, if not being ignorant. This understanding is related to the social and institutional components as well as the actual needs of the users in practice. The lack of the understanding about these aspects results in a gap between decision support developers and users at the implementation stage. We can argue that doing research is a learning process, which allows for the exploration of scientific innovations and, at the same time, the achievement of academic excellence. We can even say that we must safeguard the solidarity of science. But, if we, as research scientists, are determined to contribute to the management of societal problems, we should genuinely doing so and move beyond the normative way of thinking. Without doing this, decision support efforts remain as academic exercises and research scientists are perpetually trapped in a vicious cycle. This is a cycle, in which researchers compete for big fundings by proposing decision support projects with a list of promised benefits. The projects are launched and are carried out by multidisciplinary research groups. When the projects end after a few years, the promised benefits fail to be achieved, the outputs are not accepted, and new projects are proposed and launched again.
4 ii In this study, I intend to critically analyse how decision support have been and how decision support are. Additionally, I attempt to demonstrate the possibility of promoting the practice of scientific decision support for decision makers in practice through collaborative knowledge management. Freiburg, in February 2009 Tuck Fatt Siew
5 iii Acknowledgements First of all, I am deeply grateful to German Academic Exchange Service (DAAD) for offering me a great opportunity to pursue a doctorate degree in Germany. The study would not have been possible without its financial support. Besides, I have also been able to explore my life to a great extent in a new environment and be able to fulfil what I have been yearning for under this scholarship programme. I would like to thank my supervisor Prof. Dr. Gerhard Oesten for accepting me as a student at the Institute of Forestry Economics and for giving me constructive suggestions and comments on my work. My gratitude also goes to Dr. Frank Ebinger, who was my supervising tutor, for sharing his critical thoughts, comments, and understandings. To all my colleagues and other individuals, with whom I become acquainted, I am very thankful for their advice, help, and amicability. Working in a culturally and linguistically diverse environment is certainly challenging, but not less interesting. The experience gained is unforgettable and helps to develop my personality. My special thank is also extended to the staff at the LUWG, especially Mr. Christoph Linnenweber and his former colleague, Mr. Heiko Franke, as well as individuals coming from other institutions, all who rendered assistance during the course of my research. Last but not least, I would like to express invaluable gratefulness to my family and friends for being very supportive and having trust on me. My personal note of appreciation goes specifically to Wolfgang and his family for motivating me, giving me strength, and sharing with me the culture and life in Germany. Life is wonderful!
6 iv Abstract Appropriate scientific outputs, including decision support tools, methods, and knowledge must be effectively produced and transferred through science-management interfaces in order to be applied successfully in the management of societal problems. Often, the implementation of scientific outputs in practice is very limited. The problem has been attributable to the existence of an interface gap between science and management domains. The challenge for scientists is, apart from purposeful innovations, to address the cognitive and socio-cultural differences between and within these domains. This study attempts to address two research questions: What are the reasons for the non-integration of scientific outputs in practice? How should the underlying integration problem in the complex and uncertain decision-making environment be addressed holding on the premise that scientific data, information, knowledge, and wisdom are the fundamental resources in societal decision-making? The main objective of this study is to develop a strategy for promoting scientific decision support for decision makers in practice through collaborative knowledge management. The study adopts a reflexive methodology encompassing the approaches of theoretical analysis of the main issue and model building for addressing the problem concerned, both of which are associated with deductive logic and case study. It is a science of science research. This thesis is arranged in six chapters: Chapter 1: Introduction, including problem statement; Chapter 2: Theoretical background; Chapter 3: Conceptual framework; Chapter 4: Case study; Chapter 5: Conceptual framework reformulation; and Chapter 6: Research outlook. A systemic or systems approach using systems thinking is adopted in this study to address the interrelated issues of the complex problem situation. Applying the holistic thinking, cross-disciplinary literatures of related issues are critically analysed. The scope of the analysis encompasses scientific decision support approaches and innovations, the use for scientific data, information, knowledge, and wisdom as decision support resources, and the challenges to the integration of scientific outputs into practice. With regard to the challenges to integration, the influences of the paradigms of scientists and the rationality of decision makers on knowledge generation and utilisation are analysed. Considering the cognitive and socio-cultural differences between two different knowledge entities, a conceptual framework of collaborative knowledge management is presented as a means to promote scientific decision support for decision makers in practice. The concepts and theories of Unbounded Systems Thinking, Knowledge Management Cycle, sense-making, and interfacing agents are applied in the development
7 v of this framework. The conceptual framework is compared with the actual practice of decision support referring to a case study of integrated flood management in Rhineland- Palatinate, Germany. The discrepancies between the theoretically developed conceptual framework and the one derived from the case study are identified. The findings highlight the lack of existing mechanisms both for the articulation of tacit knowledge and for direct feedback between decision support developers and users of Informationspaket. Additionally, the differences between the developers and users as well as between a consultant and a research scientist are also identified using the same case study. The findings of the case study indicate that the management domain does not reject or deny scientific innovation or knowledge. But this domain (constituting different levels of authorities with diverse institutional roles and responsibilities) is rather reluctant to adopt scientific outputs produced by researchers, which could probably be attributable to three main reasons. Firstly, the practitioners cannot associate themselves well with third party products, especially when the products do not meet the pragmatic standard. Being pragmatic emphasises the development of non-complicated models or assessment methods based on the availability of data. Secondly, the practitioners are often not convinced by the results generated by computer models due to incompatible scales. Thirdly, the practitioners, especially those at the implementing level, need intensive personal and professional expertise support. In this respect, getting the contributions of scientists to be recognised and accepted in practice is challenging. There are a number of possible preconditions for promoting successful collaboration between science and management domains, thereby promoting the integration of scientific outputs in decision-making processes. Scientists need to: (1) get strategic partners or champions involved in a project; (2) demonstrate their capabilities, professional competency, and long-term commitment to a project; (3) conform to practical norms; and (4) demonstrate the ability to provide expertise knowledge or scientific wisdom in practice. Whilst fulfilling these preconditions is resource-demanding, changing the quality of the relationship between science and management is a long-term process. A reformulated conceptual framework, together with the best practice of decision support is presented in Chapter 5. The conceptual framework is adaptive and flexible in nature. Its structure can be adjusted based on problem contexts as well as institutional scales and levels. In general, scientists and decision makers must address the institutional barriers and incentives to collaborative knowledge management. Furthermore, it is necessary to continually raise awareness of the importance of having frequent discursive and reflexive dialogues between as well as within scientists and decision makers in promoting mutual learning and reinforcing partnerships. The presentation of the reformulated conceptual framework is followed by the limitations of the study and further research.
8 vi Zusammenfassung Wissenschaftliche Resultate mit entsprechenden Entscheidungsunterstützungsinstrumenten, Methoden und Kenntnissen müssen effektiv produziert und durch Schnittstellen zwischen Wissenschaft und Praxis übertragen werden, um das Management der sozialen Probleme zweckmäßig zu unterstützen. Häufig ist die Anwendung der wissenschaftlichen Resultate in der Praxis sehr begrenzt. Das Problem ist dem Schnittstellenabstand zwischen den beiden Bereichen zuzuordnend. Abgesehen von zweckmäßigen Innovationen stellen die kognitiven und sozialkulturellen Unterschiede zwischen und innerhalb dieser Bereiche eine große Herausforderung für die Wissenschaftler dar. Diese Studie versucht zwei wissenschaftliche Fragestellungen zu beantworten: Was sind die Gründe für das Problem, dass die wissenschaftlichen Resultate nicht in die Praxis integriert werden können? Wie sollte auf das zugrunde liegende Integrationsproblem, das in einem komplizierten und ungewissen Entscheidungsfindungsumfeld existiert, eingegangen werden, mit Rücksicht auf die wissenschaftlichen Daten, Informationen, Kenntnisse und Erfahrungen als die grundlegenden Entscheidungsfindungsressourcen? Die Zielsetzung dieser Studie ist, eine Strategie durch kooperatives Wissensmanagement zu entwickeln, um die wissenschaftliche Entscheidungsunterstützung für die Entscheidungsträger in der Praxis zu fördern. Die Studie wendet eine Reflexivmethodologie in Bezug auf den Forschungsprozess an. Die Fragestellungen werden durch die Ansätze von einer theoretischen Analyse und einer Modellbildung, die mit deduktiver Logik und Fallstudie assoziiert ist, beantwortet. Die Forschung ist Wissenschaft der Wissenschaft. Diese Dissertation wird in sechs Kapitel eingeteilt: Kapitel 1: Einleitung, einschließlich Problemstellung; Kapitel 2: Theoretische Grundlage; Kapitel 3: Konzeptioneller Rahmen; Kapitel 4: Fallstudie; Kapitel 5: Darstellung des überarbeiteten konzeptionellen Rahmens; und Kapitel 6: Ausblick. Ein systemisches Vorgehen, nämlich Systemisches Denken, wird in dieser Studie angewandt, um die zusammenhängenden Themen der komplizierten Problemsituation zu erklären. Basierend auf diesem Denken werden interdisziplinäre Literaturen kritisch analysiert. Der Umfang der Analyse zeigt die wissenschaftlichen Entscheidungsunterstützungsansätze und Innovationen, die Anwendung der wissenschaftlichen Daten, Informationen, Kenntnisse und Erfahrungen als Entscheidungsunterstützungsressourcen bzw. die Herausforderungen zur Integration der wissenschaftlichen Resultate in die Praxis. Hinsichtlich der Herausforderungen zur Integration werden die Einflüsse der unterschiedlichen Paradigmen der Wissenschaftler
9 vii auf Wissenserzeugung und die Rationalität der Entscheidungsträger auf die Anwendung des Wissens analysiert. In Betrachtung der kognitiven und soziokulturellen Unterschiede zwischen Wissenschaftlern und Praxis wird ein konzeptioneller Rahmen des kooperativen Wissensmanagements dargestellt. Die Funktion des Rahmens ist es, die wissenschaftliche Entscheidungsunterstützung für die Entscheidungsträger in der Praxis zu fördern. Konzepte und die Theorien wie das Unbegrenzte Systemische Denken, der Wissensmanagementzyklus, das Sensemaking und die Kopplungsagenten werden in der Entwicklung dieses Rahmens angewendet. Der konzeptionelle Rahmen wird mit der Darstellung der Entscheidungsunterstützung in der Praxis - die Ergebnisse einer Fallstudie des integrierten Flutmanagements in Rheinland-Pfalz, Deutschland - verglichen. Die Diskrepanzen zeigen auf, dass die Mechanismen für eine Umwandlung des Impliziten Wissens und für eine direkte Rückmeldung zwischen Entscheidungsunterstützungsentwicklern und Benutzern des Informationspaketes nicht vorhanden sind. Zusätzlich werden die Unterschiede zwischen den Entwicklern und Benutzern in der Praxis sowie zwischen einem Fachberater und einem Wissenschaftler dargestellt. Die Ergebnisse der Fallstudie zeigen auf, dass der Managementbereich die wissenschaftlichen Innovationen und Erkenntnisse nicht zurückweist. Aber dieser Bereich, der aus verschiedenen Organisationsebenen besteht, hat oft Schwierigkeiten, die wissenschaftlichen Resultate anzunehmen. Das liegt vermutlich an drei Gründen. Erstens können die Entscheidungsträger Fremdprodukte nicht gut annehmen, insbesondere wenn diese nicht die entsprechenden, d.h. von einem pragmatischen Standard abweichenden Produkte sind. Die Entscheidungsträger heben hervor, dass die Wissenschaftler bei der Entwicklung eines Modells oder einer analytischen Methode auf die Verfügbarkeit der Grundlagendaten Rücksicht nehmen sollten. Der Aufbau der Modelle sollte auch nicht kompliziert sein. Zweitens sind die Entscheidungsträger häufig nicht von den Resultaten eines Modells wegen inkompatibler Maßstäbe überzeugt. Drittens benötigen die Entscheidungsträger, insbesondere die auf der Umsetzungsebene sind, intensive persönliche und fachliche Unterstützung. In dieser Hinsicht ist es eine Herausforderung, die Akzeptanz von wissenschaftlichen Resultaten in der Praxis zu fördern. Es gibt einige Voraussetzungen für die Förderung einer erfolgreichen Zusammenarbeit zwischen Wissenschaft und Praxis, wodurch die wissenschaftlichen Resultate in die Praxis besser integriert werden können. Wissenschaftler sollten: (1) strategische Partner oder Champions in ein Projekt mit einbeziehen; (2) ihre Fähigkeiten, Fachkompetenz und langfristige Verpflichtung zu einem Projekt beweisen; (3) die praktischen Normen berücksichtigen; und (4) die Fähigkeiten, Fachkenntnisse und wissenschaftliche Erfahrungen in der Praxis zur Verfügung stellen. Das Erfüllen dieser Voraussetzungen ist anspruchsvoll und enthält große Ressourcen. Dabei stellt die Förderung eines guten Verhältnisses zwischen Wissenschaft und Praxis einen langfristigen Prozess dar.
10 viii Ein neuformulierter konzeptioneller Rahmen, zusammen mit dem bewährten Verfahren der Entscheidungsunterstützung, wird in Kapitel 5 dargestellt. Der konzeptionelle Rahmen ist anpassungsfähig und flexibel. Die Struktur kann an Problemzusammenhänge sowie Institutionsskalen und Ebenen adaptiert werden. Im Allgemeinen müssen die Institutionsbarrieren und die Anreize für ein kooperatives Wissensmanagement zwischen Wissenschaftlern und Entscheidungsträgern in der Praxis beachtet werden. Außerdem ist es notwendig, das Bewusstsein von der Bedeutung der häufigen Diskursiv- bzw. Reflexivdialoge zwischen den sowie innerhalb der Bereiche der Wissenschaftler und Entscheidungsträger kontinuierlich zu fördern, um das gegenseitige Lernen und die Kopplung der beiden Bereiche zu verstärken. Im Anschluss an die Darstellung des neuformulierten konzeptionellen Rahmens werden die Grenzen der Studie und der Forschungsbedarf aufgezeigt.
11 ix Table of Contents Preface Acknowledgements Abstract Zusammenfassung Table of Contents List of Figures List of abbreviations i iii iv vi ix xi xii 1 INTRODUCTION Problem statement Objectives and research questions Structure of argumentation Methodology 5 2 THEORETICAL BACKGROUND: Decision support in the face of complex societal problems Systems thinking The complexity of societal problems Decision support Post-war development of decision support: from machine age to systems age Decision support vis-à-vis decision-making Decision support tools Decision support process Considerations in decision support tools innovations Scientific data, information, knowledge, and wisdom as decision support resources Data, information, knowledge, wisdom The need for scientific data, information, knowledge, and wisdom in societal decision-making Integrating scientific knowledge into practice The challenges to the integration of scientific outputs into practice Science-management interface The Paradigm Lock in the interface Epistemic community in the science domain Science Epistemic community Bounded rationality in the management domain Summary 37 3 CONCEPTUAL FRAMEWORK: Scientific decision support for decision makers in practice through collaborative knowledge management Fundamental concepts The Unbounded Systems Thinking Knowledge Management Cycle Sense-making Interfacing agents 47
12 x 3.2 The conceptual framework of collaborative knowledge management The organisation The components and their roles The process of knowledge generation, mediation, utilisation, and conversion The interaction between the components and processes 53 4 CASE STUDY: Decision support for integrated flood management in Rhineland-Palatinate, Germany Integrated flood management in Rhineland-Palatinate: Aktion Blau Informationspaket Methods 59 Limitations of the case study The participants of Informationspaket State water manager, LUWG Regional water manager, Struktur- und Genehmigungsdirektion (SGD) Land manager, DLR Westpfalz The consultants Presentation of results from interviews Interview with the state water manager, LUWG Interviews with the land managers, DLR Westpfalz Interviews with the consultants Findings Depiction of decision support in practice The organisation The components and their roles The process of information flow Discrepancies between conceptualised framework of scientific decision support and decision support in practice The terms information and knowledge The articulation of tacit knowledge The interface Feedback mechanism The differences between decision support developers and users in practice The differences between consultants and research scientists 80 5 CONCEPTUAL FRAMEWORK REFORMULATION Experience derived from the case study The reformulated conceptual framework 86 6 RESEARCH OUTLOOK Limitations of the study Further research 90 Bibliography 91 Appendix 1: Structure of administration Rhineland-Palatinate 98 Appendix 2: Guides on designing and conducting semi-structured interview 99 Appendix 3: Interview guides (in German) 101 3a: State water manager, LUWG 101 3b: Land manager, DLR Westpfalz 103 3c: Consultants 105
13 xi List of Figures Figure 1: Structure of argumentation of the thesis Figure 2: The three dimensions of complex unstructured problems (Kolkman et al., 2005) Figure 3: The interaction between science and management domains in an informal organisation through an interface Figure 4: The paradigm lock illustrated by UNESCO (in Acreman, 2005) Figure 5: The views of managers and scientists about each other (Roux et al., 2006) Figure 6: Theoretical problems at the science-policy intersection and normative requirements for the interfaces (van den Hove, 2007) Figure 7: Characteristics of mode 1 and mode 2 science (Gibbons et al., 1994) Figure 8: Communities of knowledge generation (summarised and adapted from Kolkman et al., 2005) Figure 9: Mitroff/Linstone UST model (Hall et al., 2005) Figure 10: Knowledge management cycle model (King et al., 2008) Figure 11: The Knowing Organization (Choo, 1996) Figure 12: The general layout of the human and technical components of the informal organisation under the perspective of personal, technical and organisational Figure 13: The science loop the process of knowledge generation and the mediation of knowledge utilisation initiated by the science domain Figure 14: The management loop is added to the science loop representing the process of knowledge utilisation in the management domain Figure 15: The complete layout of the conceptual framework of collaborative knowledge management by adding the feedback or interaction loop to illustrate the interconnections between personnels Figure 16: Aktion Blau for integrated flood management in Rhineland-Palatinate, Germany (in German; adapted from LUWG, 2005: p.11) Figure 17: Information and knowledge flow through Informationspaket for integrated flood management in Rhineland-Palatinate, Germany Figure 18: The differences between decision support developers and users in practice Figure 19: The differences between a consultant and a research scientist Figure 20: Reformulated conceptual framework of collaborative knowledge management
14 xii List of abbreviations CoP DIKW DLR DSS EU GIS HELP ICT IFM IWRM LAWA LUWG MUFV MWVLW NoP OLAP OR PSS R&D SDSS SGD UNESCO UST WFD WMO Communities of Practice Data, information, knowledge, wisdom Dienstleistungszentrum Ländlicher Raum Decision Support Systems European Union Geographic Information Systems Hydrology for the Environment, Life and Policy Information, communication and technology Integrated Flood Management Integrated Water Resources Management Länderarbeitsgemeinschaft Wasser Landesamt für Umwelt, Wasserwirtschaft und Gewerbeaufsicht Ministerium für Umwelt, Forsten und Verbraucherschutz Ministerium für Wirtschaft, Verkehr, Landwirtschaft und Weinbau Networks of Practice On-Line Analytical Processing Operation Research Planning Support Systems Research and Development Spatial Decision Support Systems Struktur- und Genehmigungsdirektionen United Nations Educational, Scientific and Cultural Organization Unbounded Systems Thinking Water Framework Directive World Meteorological Organization
15 INTRODUCTION 1 1 INTRODUCTION 1.1 Problem statement Planners and decision makers involving in the management of environmental problems are confronted with planning and decision-making tasks, which entail far-reaching and interconnected consequences on the society, environment, and economics. These tasks are concerned with the design, selection, and implementation of potential alternatives of problem solutions requiring trade-off analysis of often conflicting goals. In the context of integrated flood management, which sits within the broader context of integrated water resource management (UNESCO, 2007), planners or decision makers need to devise plans or make decisions about possible flood mitigating measures. The potential measures aim to reduce human and socio-economic losses from flooding and use of flood plains while increasing social, economic, and ecological benefits. Executing the demanding management tasks requires a resourceful and resource-rich decision-making capacity. This capacity is supported by large bodies of cross-cutting information and knowledge as well as coherent relationships between science, politics, public administrations, and other stakeholders. Considering the needs to manage complex environmental or societal problems 1, research scientists have been striving to develop modelling, simulation or decision support systems to provide better scientific information and knowledge as well as to facilitate decision makers in comprehending problems, analysing decisions, and predicting decision outcomes. Relating to group decision-making, computer-based tools are also designed for facilitating group discussion, and thereby increasing satisfaction and compromise so as to obtain an inclusive, equitable and defendable decision (Walker et al., 2001). Building on the expectations of what decision support tools could achieve, significant amount of money has been invested in decision support projects. At the European level, the European Union Fifth Framework Programme has invested about 250 million Euro in 180 river basin modelling projects to address issues stipulated in the European Water Framework Directive (WFD) 2 (Borowski and Pahl-Wostl, 2008). Despite the advanced innovation in computer-based tools, the implementation of the developed tools has, hitherto, limited success 3 in practice. They fail to fulfil expectations across many of the domains in which it has been applied (Walker, 2002). The failure of the end products of the vast number of decision support projects, which are sometimes overlapping (Szaro et al., 1998), are attributable to irrelevance, inflexibility, inaccessibility 1 Environmental problems are defined as being basically social problems (Timmerman and Langaas, 2005). 2 One of the main types of tools available to support the WFD implementation consists of mathematical simulation models for river catchment (Willems and de Lange, 2007). 3 Finlay and Forghani (1998) suggested success is equated with repeat use and user-satisfaction.
16 2 INTRODUCTION of the tools as well as the lack of confidence of the users and institutional barriers. The status quo of decision support tools may reflect what Biswas (1975) reckoned about 30 years ago: the purpose of the models developed could be classified somewhere between dilettantism and academic exercises. The investigation of the failure in decision support projects is rarely conducted. It has been argued that the non-application of decision support tools in practice cannot be described as unsuccessful. Success is difficult to be defined (Todd, 2001 and Moore, 1996; in Janssen et al., 2006). Walker (2002) argued that it is a significant challenge in its own right as failure is rarely reported and analysed. According to Walker (2002), many DSS initiatives that have not resulted in significant operational use have nevertheless been judged to have been successful on the basis of the learning achieved by the developers and users, particularly where development has been highly participatory. This, however, does not justify the doubts of decision makers who require practical tools, credible information and knowledge to deal with the urgency of public decisionmaking. The antagonistic views about the achievement of decision support projects have recently been reflected through the research on science-policy or science-management interface gap. This emerging research is particularly concerned with the comprehension of cognitive and socio-cultural differences between science and practice. It has been debated that these differences have caused the non-delivery and non-adoption of scientific outputs in practice. In light of the importance of addressing this soft aspect, the implementation of decision support tools must embrace procedures that can deal with the complexity of organisational decisions of future and go beyond the technical orientation of previous decision support systems (Courtney, 2001). Courtney (2001) argued that a major overhaul of the conventional DSS scarcely considers anything but the technical perspective. The limitations inherent in the underlying methodologies in the technical or hard approach become one of the barriers to DSS research (Ho and Sculli, 1997). Addressing the interface problem within an organisation, Courtney (2001) voiced out the need for a new decision-making paradigm for DSS by integrating knowledge management. The multiple perspective approach of knowledge management as a strategy for decision support in an informal organisation, in which science and management domains co-exist, has not been explored. The strategy of collaborating decision support and knowledge management should serve three purposes. Firstly, it should bridge the interface gap between science and management domains. Secondly, it should encourage the effective use of scientific outputs in light of the challenges to harmonise the concurrent implementation of different European Directives at the national and local levels, for example the EU Flood
17 INTRODUCTION 3 Directive 4 and the EU Water Framework Directive 5, which are concerned with integrated flood management and water management, respectively. Thirdly, it should encourage the efficient use of human and funding resources. In this respect, this study attempts to answer the following main questions: What are the reasons for the non-integration of scientific outputs in practice? How should the underlying integration problem in the complex and uncertain decision-making environment be addressed holding on the premise that scientific data, information, knowledge, and wisdom are the fundamental resources in societal decision-making? 1.2 Objectives and research questions The main objective of this study is to develop a strategy for promoting scientific decision support for decision makers in practice through collaborative knowledge management. This study does not aim to develop decision support systems for supporting decision-making processes. Instead, it focuses on the integration of science and management domains as well as the application of scientific outputs in practice. The specific objectives of this study are: To seek theoretical explanations for the non-integration of scientific outputs into practice; To propose a conceptual framework of collaborative knowledge management as the basis for scientific decision support; To compare the proposed conceptual framework with a practical example using the case of integrated flood management; To recommend best practice for promoting the integration of scientific outputs in practice. The corresponding research questions for achieving the objectives are: What could be the underlying reasons that result in the non-integration of scientific outputs into practice? 4 The EU Flood Directive 2007/60/EC on the assessment and management of floods has been adopted since October 2007 at the EU level. The Directive calls on the member states to establish flood risk management plans by 22 December The plan shall be coordinated at the level of the river basin district or unit of management. The Directive emphasises the quantitative aspect of flood risk management. 5 The EU Water Framework Directive 2000/60/E is a water legislation produced by the European Commission as a driver for achieving sustainable management of water in the Member States. All inland and coastal waters within defined river basin districts must reach at least good status by 2015 through the establishment of environmental objectives and ecological targets for surface waters taking into consideration environmental, economic and social aspects.
18 4 INTRODUCTION What constitutes a conceptual framework of collaborative knowledge management and how does it function? What are the discrepancies between the proposed conceptual framework and the practice of decision support through knowledge management in the case of integrated flood management? What is the best practice for promoting the integration of scientific outputs into practice? 1.3 Structure of argumentation This thesis constitutes altogether six chapters which are illustrated in Figure 1. Chapter 1 introduces the problem to be studied, the objectives to be achieved, the organisation of the thesis, and the methodology used in this study. Chapter 2 provides a theoretical background of the study through cross-disciplinary literature. The problem addressed in this study deals with situations which are bound with system complexity and scientific controversies. The complexity of the problem conveys the notion that the problem is not concerned with the discovery of particular facts, but with the comprehension of the management of an inherently complex reality (Kolkman et al., 2005). The limited or unstructured understanding of the system is a source of dispute and controversy (Slob et al., 2007). Chapter 1: Introduction Chapter 2: Theoretical background Chapter 3: Conceptual framework Chapter 4: Case study Chapter 5: Conceptual framework reformulation Chapter 6: Research outlook Figure 1: Structure of argumentation of the thesis.
19 INTRODUCTION 5 Based on these notions, Chapter 2 begins with the introduction on the concept of systems thinking and the characteristics of the complexity of societal problems. The understanding of the concept of systems thinking is imperative as the essence of this thesis constitutes various aspects, which have to be linked in a holistic way. Descriptions on the complexity of societal problems, on the other hand, serve the purpose of providing a broad understanding about the problems associated with decision support and the possibility of capitalising on scientific knowledge to promote scientific decision support for decision makers in practice. Other sections in Chapter 2 focus on providing for critical theoretical analysis pertaining to decision support leading to the analysis of the science-management interface problem. The theoretical analysis is followed by the development of a conceptual model incorporating relevant concepts from the management field (Chapter 3). The second part of the dissertation presents a case study of integrated flood management in Rhineland-Palatinate, Germany (Chapter 4). This study does not adopt the conventional approach of deriving lessons learned from the involvement in a decision support project initiated by an academic research team. Instead, it synthesises the practical experience gained by the practitioners themselves, who are involved in the development of a decision support instrument for use in practice. Semi-structure interviews were conducted with the practitioners, in addition to document analysis and participation in a workshop. By synthesising the results from the case study, the practice of decision support and knowledge management is formulated and compared with the theoretical conceptual framework. Simultaneously, the case study is also used to find out the differences or similarities between decision support developers and users in practice as well as between consultants and academic researchers. Subsequently, a reformulated conceptual framework is derived and the best practice for decision support is suggested (Chapter 5). The thesis ends with the discussion on the limitations of the study and the recommendations for further research (Chapter 6). 1.4 Methodology Most of the studies addressing the problem of integrating scientific outputs into practice have been conducted in the broader context of integrated water resources management or in other field of natural resources management. Little is known about the science and management interface problem in the context of integrated flood management. Although the contexts of the management issues are different, the nature of the sciencemanagement interface problem encountered in the different types of management is similar. The nature of the problem is also comparable to that in the field of management sciences. This study as a whole is exploratory and reflexive in nature. Reflexive methodology is concerned with the importance and role of interpretation and reflection during the research process (Alvesson and Sköldberg, 2000). This approach aims to produce more
20 6 INTRODUCTION interesting and unexpected research results by re-thinking conventions and open up for more varied and challenging uses of research questions, fieldwork practices, modes of interpretations, and styles of writing. For addressing the research questions, this study adopts the approaches of theoretical analysis of the main issue and model building for addressing the problem concerned, both of which are associated with deductive logic and case study. In general, this is a science of science research to address the need for better tools, methods, and knowledge for encouraging the efficacy and impact of science and technology on societal decision-making.