People’s lives involve struggling with self-control. The trouble is that self-control requires a lot of motivation. This project researches a key component of self-control that does not rely on peoples’ motivations. By understanding the cognitive mechanisms underlying self-control, we can develop tools to improve individuals’ financial, health, and environmental decisions.

The placebo effect is an apparent improvement in someones health/behavior that is not due to an active substance but rather to someones belief or expectation that (s)he will improve. Research on placebo effects is scarce, because placebos cannot be ?patented? or commercialized. Placebos are often cast as the villains to beat in medical research, although the placebo effect is a phenomenon that could guide us toward a better understanding of health/behavioral improvements. By conducting a series of longitudinal laboratory experiments, I aim to investigate to what extent placebo effects are enduring rather than transient phenomena. On the one hand, it could be expected that experiencing a placebo effect attenuates the strength of subsequent placebo effects. On the other hand, (un)favorable experiences could sustain or even amplify expectancies in subsequent experiences. Direct experience with a placebo could therefore attenuate but also amplify subsequent placebo effects. In addition to documenting the persistence of placebo effects, this research aims to outline which factors moderate the longitudinal effects of placebo effects. Depending on the ambiguity of the experience, expectancies may be updated over time by post-experience information. This research will yield significant theoretical contributions, not only for marketing academics, but for fundamental research in psychology and medicine as well. Moreover, this research project will yield important practical insights for public policy makers, marketing practitioners, drug developers and citizens alike.

Social sciences are engaging in efforts to scrutinize the very processes of creation of scientific knowledge. Whereas much of the attention is revolving around questionable or suboptimal practices of data analysis, how data is collected from human participants received less scrutiny. Data collection in psychological, sociological, and management sciences is dramatically changing, moving progressively away from physical environments and towards the Internet. Researchers can now crowdsource data collection, recruiting and compensating people on Internet marketplaces (e.g., Mechanical Turk) for participating in their investigations. Today, it is customary to read articles in the highest ranked journals that are partially or entirely based on crowdsourced data. Crowdsourced investigations are inherently characterized by a lack of controllability over the research process. Researchers have little control over the sampling and execution stages of research, and largely ignore how their recruitment and design decisions affect the characteristics of the samples they collect and of the data they obtain from such samples. This poses unique challenges to the scientific validity of crowdsourced investigations that are currently unaccounted for. This project investigates these challenges, exploring how the characteristics and the quality of crowdsourced data depend on features of the crowdsourcing process that are universally relevant across research paradigms and crowdsourcing platforms. In particular, I will focus on the role of the incentives used to compensate participants, of the time (in the day, in the week) in which research is crowdsourced, and of participants research history. This will result in theoretically grounded and practically actionable insights to conduct more valid crowdsourced investigations. The Internet democratized science by lowering the barriers to its consumption and dissemination. Crowdsourcing can now allow a more democratic production of scientific knowledge. However, crowdsourcing has many undetected pitfalls. My ultimate goal is to contribute to better science by improving the quality of online research.

People’s lives are pervaded with quantitative information. For example, practically all products and services carry quantitative information ranging from nutritional content (e.g., “60 grams sugar content") to ecological impact (e.g., “6 liters/100 kilometers fuel consumption”). This widespread provision of quantitative information is generally encouraged because it should allow for better-informed decisions. In contrast with this assumption, a growing research stream has been documenting a large number of errors in decisions involving quantitative information. As such, the well-intentioned provision of quantitative information may be ineffective in improving individuals’ decisions. By using a combination of online, lab and field studies, this project will investigate how the metric (e.g., grams vs. cubes of sugar) in which information is described improves understanding of quantitative information. A first subproject will test whether metrics that lead individuals to think of quantitative information as a collection of discrete elements (a set of cubes, piles, bags…) engender a more natural understanding of quantitative information than nondiscrete metrics (liters, grams, pounds). A second subproject will investigate how using a discrete metric renders quantitative information to be more important in decisions: Do individuals rely more on quantitative information about sugar content when it is described in cubes than in grams? In a third subproject, I will focus on how discrete metrics may be particularly effective in two contexts in which most decision errors occur: due to a temporal (cognitive busyness) or chronic lack of ability (numeracy: the ability to process numerical concepts). For theory, the project will be among the first to turn attention to how individuals’ understanding of quantitative information and decision making can be improved. For practice, this project will provide policy makers with an easy-to-implement, yet relevant intervention that is applicable to all domains involving quantitative decision making.

The central objective of SLIFF is to enhance the sustainability of fresh food logistics by improving logistics resource utilization while distribution lead times are reduced. The project addresses two prevalent challenges in this context: 1. Optimization of network logistics versus optimization at distribution centre (DC); 2. Inventory optimization versus transport optimization. Distribution network planning and local DC planning problems have been largely addressed separately in science and practice. However, inventory reduction policies have resulted in a just-in-time flow of products ? with little or no storage buffers throughout the distribution network. This requires a synchronization of network level and local DC logistics planning. SLIFF aims to propose (distributed) decision-structures and develop optimization models to facilitate such synchronization. Another novelty of SLIFF resides in acknowledging that vendors may share both transportation resources and storage space at the DC in the replenishment of the DC. Accordingly, SLIFF aims for joint optimization of transportation, inventory, and material handling resources. These resources are generally owned by many independent parties. Therefore, this requires research in the domain of: " Organization design; because the vendors, retailer and logistics service providers are autonomous companies " Optimization models; because decision support for multiple interdependent decisions is needed " ICT interoperability; because parties will be heavily interacting to synchronize decisions The partner retailer has recently contracted an LSP who, in the future, could act as a cross chain control centre ? coordinating transportation and inventory optimization for the many vendors involved. Insights from SLIFF are disseminated via academic channels, professional societies and branch organizations.