Open Access

Coopetitive endeavors offer valuable strategic options for firms. Yet, many of them are failure-prone as partners must balance collective and private interest. While interpartner trust is considered central for alliance success, paradoxically, the role and dynamics of trust is still not understood. We synthesize a computational model, capturing relational dynamics of an alliance, encompassing coevolution of trust, partner contributions, and (relative) alliance interactions. Analyzing alliance dynamics using simulation we find and explore a tipping boundary, separating a regime of alliance failure and success. We identify implications for collaborative (aspirations) and private strategies (openness). Our analyses reveal that strategies informed by a static mental model of partner trust, contributions, and openness tend to yield subpar alliance results and hidden failure-risk. We discuss implications for management theory.

In 2018, the Intergovernmental Panel on Climate Change (IPCC, 2018) found that rapid decarbonization and net negative greenhouse gas (GHG) emissions by mid-century are required to "hold the increase in global average temperature to well below 2°C above pre-industrial levels and pursue efforts to limit the temperature increase to 1.5°C," as stipulated by the Paris Agreement (UNFCCC, 2015, p. 2). Meeting these goals reduces physical climate-related risks from, for example, sea-level rise, ocean acidification, extreme weather, water shortages, declining crop yields, and other impacts. These impacts threaten our economy, security, health, and lives. At the same time, policies to mitigate these harms by rapidly reducing GHG emissions can create transition risks for businesses - for example, stranded assets and loss of market value for fossil fuel producers and firms dependent on fossil energy (Carney, 2019). Rapid decarbonization requires an unprecedented energy transition (IEA, 2021a) driven by and affecting economic players including businesses, asset managers, and investors in all sectors and all countries (Kriegler et al., 2014). However, GHG emissions are not falling rapidly enough to meet the goals of the Paris Agreement (Holz et al., 2018). The UNFCCC, 2021 found that the emissions reductions pledged by all nations as of early 2021 "fall far short of what is required, demonstrating the need for Parties to further strengthen their mitigation commitments under the Paris Agreement" (2021, p. 5). Businesses are faring no better. Despite high-profile calls to action from influential firms such as BlackRock (Fink, 2018, 2021), corporate action to meet climate goals has thus far fallen short (e.g. the Right, 2019 analysis of the German DAX 30 companies' emissions targets by NGO "right."). Instead of implementing climate strategies that might mitigate the risks, managers are often caught up in "firefighting" and capability traps that erode the resources needed for ambitious climate action (Sterman, 2015). Firms may also exaggerate environmental accomplishments, leading to greenwashing (Lyon and Maxwell, 2011); implement policies that are vague, rely on unproven offsets, or are not climate neutral (e.g. Sterman et al., 2018); or simply take no action at all (Delmas and Burbano, 2011; Sterman, 2015). Adding to the confusion are difficulties evaluating the effectiveness of different climate policies. Misperceptions include wait-and-see approaches (Dutt and Gonzalez, 2012; Sterman, 2008), underestimating time delays and ignoring the unintended consequences of policies (Sterman, 2008), and beliefs in "silver bullet" solutions (Gilbert, 2009; Kriegler et al., 2013; Shackley and Dütschke, 2012). These beliefs arise in part because the climate–energy system is a high-dimensional dynamic system characterized by long time delays, multiple feedback loops, and nonlinearities (Sterman, 2011), while even simple systems are difficult for people to understand (Booth Sweeney and Sterman, 2000; Cronin et al., 2009; Kapmeier et al., 2017). Although senior executives might receive briefings on climate change, simply providing more information does not necessarily lead to more effective action (Pearce et al., 2015; Sterman, 2011). Alternatively, interactive approaches to learning about climate change and policies to mitigate it can trigger climate action (Creutzig and Kapmeier, 2020). Decision-makers require tools and methods grounded in science that enable them to learn for themselves how a low-carbon economy can be achieved and how climate policies condition physical and transition risks. The system dynamics climate–energy simulation En-ROADS (Energy-Rapid Overview and Decision Support; Jones et al., 2019b), codeveloped by the climate think-tank Climate Interactive and the MIT Sloan Sustainability Initiative, provides such a tool. Here we show how En-ROADS helps HSBC Bank U.S.A., the American subsidiary of U.K.-based multinational financial services company HSBC Holdings plc, focus its global sustainability strategy on activities with higher impact and relevance, communicate and implement the strategy, understand transition risks, and better align the strategy with global climate goals. We show how the versatility and interactivity of En-ROADS increases its reach throughout the organization. Finally, we discuss challenges and lessons learned that may be helpful to other organizations.

It is assumed that more education leads to better understanding of complex systems. Some researchers, however, find indications that simple mechanisms like stocks and flows are not well understood even by people who have passed higher education. In this paper, we test people’s understanding of complex systems with the widely studied stock-and-flow (SF) tasks. SF tasks assess people’s understanding of the interplay between stocks and flows. We investigate SF failure of domain experts and novices in different knowledge domains. In particular, we compare performance on the original study’s bathtub task with the square wave pattern with two alternative cover stories from the engineering and business domains on different groups of business and engineering students from different semesters. Further, we show that, while engineering students perform better than business students, with progressing in higher education, students may lose the capability of dealing with simple SF tasks. We thus find hints on déformation professionelle in higher education.

It is assumed that more education leads to better understanding of complex systems. Some researchers claim, however, find indications that simple mechanisms like stocks and flows are not well understood even by people who have passed higher education. In this paper, we test people’s understanding of complex systems with the widely studied stock-and-flow (SF) tasks (Booth Sweeney and Sterman 2000). SF tasks assess people’s understanding of the interplay between stocks and flows. We investigate SF failure of domain experts and novices in different knowledge domains. In particular, we compare performance on the original study’s Bathtub task with the square wave pattern (Booth Sweeney and Sterman 2000) with two alternative cover stories from the engineering and business domains on different groups of business and engineering students from different semesters. Further, we show that, while engineering students perform better than business students, with progressing in higher education, students seem to lose the capability of dealing with simple SF tasks from domains other than their field. We thus find hints on déformation professionelle in higher education.

SF-failure, the inability of people to correctly determine the behavior of simple stock and flow structures is subject of a long research stream. Reasons for SF-failure can be attributed to different reasons, one of them being lacking domain specific experience, thus familiarity with the problem context. In this article we present a continuation of an experiment to examine the role of educational background in SF-performance. We base the question set on the Bathtub Dynamics tasks introduced by Booth Sweeney and Sterman (2000) and vary the cover stories. In this paper we describe how we developed and tested a new cover story for the engineering domain and implemented the recommendations from a prior study. We test three sets of questions with engineering students which enables us to compare the results to a previous study in which we tested the questions with business students. Results mainly support our hypothesis that context familiarity increases SF-performance. With our findings we further develop the methodology of the research on SF-failure.