Whenever intrepid researchers venture into new terrain, they find that they require knowledge outside of their formal training. This paper reviews bodies of knowledge for operations management (OM) researchers interested in the new area of Behavioral Operations. We highlight theoretical constructs and empirical phenomena from cognitive psychology, social psychology, group dynamics, and system dynamics. We also provide a guide for where to go to learn more about each body of knowledge. Our overall goal is to lower the startup costs for new researchers in Behavioral Operations.

Purpose This research aims to better understand the main drivers of entrepreneurial motivation among university students and to determine whether entrepreneurship education has a moderating effect on improving the impact of knowledge base and entrepreneurship competencies on entrepreneurial motivation. Design/methodology/approach This study uses a mixed-method approach that combines qualitative interviews and a cross-sectional survey of a sample of 465 university students. Findings The study reveals that entrepreneurship competencies are a predictor of entrepreneurship motivation but that knowledge base is not. Additionally, entrepreneurship education does not improve the motivation of university students to become entrepreneurs. These findings suggest that, to increase entrepreneurial motivation, pedagogy should emphasize the development of students’ entrepreneurial psychological and social skills by covering in particular the emotional dimension and critical thinking. Originality/value This research contributes to the literature on entrepreneurship education and provides strategic recommendations for university managers and education-policy makers.

Though often analyzed separately, supply chain instability and customer demand interact through product availability. We investigate the feedback between supply chain performance and demand variability in a model grounded in a first-hand study of the hybrid push-pull production system used by a major semiconductor manufacturer. While customers' response to variable service levels represents an important concern in industry, with sizeable impacts on company profitability, previous models exploring supply chain instability do not account for it. This research incorporates two effects of customer responses to availability. The sales effect captures the negative feedback whereby product shortages cause customers to seek alternative sources of supply, reducing demand and easing the shortage. The production effect captures the delayed impact of changes in demand on the manufacturer's production decisions: lower demand leads to reduced production, prolonging shortages that depress demand, a destabilizing positive feedback. We show how the sales and production effects interact to destabilize the supply chain and lower average performance. Supply chain models that assume exogenous demand may therefore underestimate the amplification in the chain and the value of inventory buffers. In addition, incorporating the customer response leads to different inventory and utilization policies from those in use by the company. The model yields insights into the costs of lean inventory and responsive utilization policies in the context of hybrid production systems and endogenous demand.

Several humanitarian organizations today find themselves thinly stretched in multiple protracted relief and recovery operations around the world. At the same time, the need for humanitarian relief and recovery operations is forecasted to increase dramatically in the next decades. Hence, humanitarian organizations will face increased challenges to provide assistance (e.g., assessing needs, moving the displaced, tending the wounded, restoring water and sewage systems) while trying to build and maintain capacity (e.g., hiring and training people, capturing lessons learned, structuring organizational processes). In this paper we develop a formal simulation model that quantifies the tradeoff that exists between providing assistance and building capacity in humanitarian organizations. We explore in our model the performance of two polar resource allocation strategies: one focusing on relief and recovery efforts and another focusing on capacity building. When humanitarian organizations cannot retain the knowledge gained in the field, a strategy that emphasizes relief and recovery is not enduring and leads to a better-before-worse behavior. However, if humanitarian organizations can retain a large fraction of the lessons learned in the field, they can achieve more enduring performance with a relief and recovery strategy. Nevertheless, high stress levels, caused by relief requirements significantly above those which can be made available by the organization, increase personnel turnover and limit the fraction of learning that the organization can retain, imparing a relief and recovery strategy. Our work sheds light on the tradeoff that humanitarian organizations face between providing relief and building capacity in stressful and demanding environments.

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Small Island Developing States (SIDS) face tension between economic growth and environmental impact. Tourism fuels growth, but the resulting solid waste and other pollutants threaten the SIDS' natural beauty, quality of life for residents, attractiveness to tourists, and economic success. We assess the tension between tourism-driven economic growth and environmental degradation from a limits-to-growth perspective, developing a generic system dynamics model of the problem using 38 years of data from the Maldives to estimate parameters and Monte-Carlo methods to assess the sensitivity of results to uncertainty. We contrast development paths for the next three decades under three sets of policies focusing on promoting growth, managing tourism demand-supply balance, and improving waste management. Findings are counterintuitive; policies focused on better waste management alone are self-defeating, because they increase tourism, growth and waste generation, undermining attractiveness and growth later. Policies that limit tourism demand improve economic and environmental health.

Against the backdrop of over two hundred thousand people dead or missing and millions of people homeless after China's massive earthquake and Myanmar devastating cyclone, forecasts estimate that natural and manmade disasters are likely to increase five-fold both in number and impact over the next 50 years. Hence, the need for disaster relief provided by humanitarian organizations during disasters should continue to increase.At the same time, humanitarian organizations face increased challenges scaling capacity, improving operational efficiency, reducing staff turnover, improving institutional learning, satisfying increasingly demanding donors, and operating in increasingly challenging environments, with poor or inexistent infrastructure, high demand uncertainty and little time to prepare and respond. To address such challenges, managers in humanitarian organizations must understand the complexity that characterizes humanitarian relief efforts to learn how to design and manage complex relief operations. Yet, learning in such complex and ever changing environments is difficult precisely because managers seldom confront many of the consequences of their most important decisions. Effective learning in such environments requires methods and tools that allow managers to capture important feedback processes, accumulations, delays, and nonlinear relationships, visualizing complex systems in terms of the structures and policies that create dynamics and regulate performance. The system dynamics approach provides managers with a set of tools that can help them learn in complex environments. These tools include causal mapping, which enables managers to think systemically and to represent the dynamic complexity in a system of interest, and simulation modeling, which permits managers to assess the consequences of interactions among variables, experience the long-term side effects of decisions, systematically explore new strategies, and develop understanding of complex systems.

One of the most fundamental principles in system dynamics is the premise that the structure of the system will generate its behavior. Such philosophical position has fostered the development of a number of formal methods aimed at understanding the causes of model behavior. To most in the field of system dynamics, behavior is commonly understood as modes of behavior (e.g., exponential growth, exponential decay, and oscillation) because of their direct association with the feedback loops (e.g., reinforcing, balancing, and balancing with delays, respectively) that generate them. Hence, traditional research on formal model analysis has emphasized which loops cause a particular "mode" of behavior, with eigenvalues representing the most important link between structure and behavior. The main contribution of this work arises from a choice to focus our analysis in the overall trajectory of a state variable -a broader definition of behavior than that of a specific behavior mode. When we consider overall behavior trajectories, contributions from eigenvectors are just as central as those from eigenvalues. Our approach to understanding model behavior derives an equation describing overall behavior trajectories in terms of both eigenvalues and eigenvectors. We then use the derivatives of both eigenvalues and eigenvectors with respect to link (or loop) gains to measure how they affect overall behavior trajectories over time. The direct consequence of focusing on behavior trajectories is that system dynamics researchers' reliance on eigenvalue elasticities can be seen as too-narrow a focus on model behavior -a focus that has excluded the short term impact of a change in loop (or link) gain in its analysis.