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The literature on organizational change and resistance to change is contradictory. Some scholars find resistance a hindrance to successful change (traditional paradigm), whereas others find it a valuable source (modern paradigm). The objective of this study is to enhance our understanding of how resistance affects organizational change by providing a coherent system dynamics perspective. Based on interviews, expert modelling and group modelling, this inductive case study develops a causal loop diagram that displays eight interacting feedback loops to explain resistance to change and the role that participatory strategies play in addressing this. The model contributes to the theoretical debate on how resistance affects change by providing propositions that integrate the traditional and modern paradigms. When managers face decisions about when to increase, stabilize or decrease the use of participatory strategies, our findings imply to base these decisions upon currently dominating feedback loops, such as the Stress Trap or Slow Trap. © 2018 The Authors. Systems Research and Behavioral Science published by International Federation for Systems Research and John Wiley & Sons Ltd

1In many coastal regions, activities of multiple users present a growing strain on the ecological 2 state of the area. The necessity of using integrative system approaches to understand and solve 3 coastal problems has become obvious in the last decades. Integrated management strategies 4 for social-ecological systems (SESs) call for the development of SES indicators that help (i) 5 to identify and link the states and processes of social, economic and ecological subsystems 6 and (ii) to balance different stakeholder objectives over the long-term within natural limits. 7Here we use a system dynamics modeling approach called group model building (GMB) as a The necessity of using integrative system approaches to understand and solve environmental 32 problems has become obvious in the last decades. The development of knowledge for Increasing our insight into complex socio-ecological systems helps to understand 59 environmental problems better, but is not sufficient for solving them. We also need to 60 motivate stakeholders to take action. There is increasing demand for participation of 66Model building is used more and more as a tool to structure discussion and debate about 67 issues, and to create a learning environment that allows assumptions to be tested. Participative Group model building as a tool for understanding SES issues 110In this study we want to explore the SD methodology as a tool for ICM and indicator As is the case for many coastal areas around the world, management issues in the Wadden Sea 119 region can be considered as "wicked", "unstructured" or "messy" (Kabat et al. 2012). Such 141In the following section we introduce our study area and cases and describe their SES 142 characteristics. Next we explain the method of group model building and how we applied it. 143In the fourth section we present the GMB models and key variables for SES indicators.

-Even with the rapid changes in the level of complexity and the uncertainty of the environment in which Belgian sea fisheries operate, fisheries management in Belgium is still mainly based on restrictive policy instruments founded in the biological approach of fisheries management science. Since they will continue to play an important role, this paper evaluated changes in three restrictive policy instruments and their effect on future fleet performance and dynamics, i.e. maximum fishing days, total quota-restrictions and licences. These effects are tested through scenarios in a microeconomic simulation model, including sensitivity analysis. This study opts for a dynamic simulation model based on a microeconomic approach of fleet dynamics using system dynamics as a modelling technique (operational base: Vensim r DSS). The results indicated that changes in maximum fishing days and total quota resulted in higher fluctuations in fleet performance and dynamics compared to changes in licences. Furthermore, changes in maximum fishing days and total quota had a direct impact on fleet performance, though not always as expected, whereas licences only affected fleet performance indirectly since they only limit the entry of new vessels to the fleet and they can block the growth of successful sub fleets. The outcomes of this study are translated into practical recommendations for improving fisheries management. Firstly, policy makers need to be more aware of misperceptions of feedback. Secondly, the results proved that altering only one type of restrictive policy instrument at a time often fails to meet desired outcomes. Therefore, policy makers need to find a balance in combining policy instruments. Finally, this paper opens the discussion on the future value of restrictive policy instruments in the rapidly changing, complex and uncertain fisheries environment. It suggests rethinking their use from "preserving a status quo and social peace" toward a driving factor in "stimulating fleet dynamics".

Dynamic stock control tasks have been frequently used in laboratory experiments in behavioural research to investigate understanding of dynamic systems. In these studies, the dynamic system is often represented in the form of a simulation model, and they almost exclusively focus on how the structure of a system (i.e. the simulation model) affects human's inference of system behaviour. In doing so, these studies fail to consider that human's performance on dynamic decision making tasks might also be a function of the complexity embedded in other task components like goals, input, processes, output, time and presentation. Hence, the objective of this paper is to carve out what task complexity entails when applied to dynamic stock control tasks in order to determine its usefulness for future research on human understanding of such tasks. In this paper, task complexity is conceptualized consisting of ten complexity dimensions: (1) size; (2) variety; (3) redundancy; (4) ambiguity; (5) variability; (6) inaccuracy; (7) novelty; (8) incongruity; (9) connectivity; and (10) temporal demand. 2 These factors correspond to Liu and Li's (2012) 'Complexity contributory factors'. Syst. Res