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Firms often partially fund new projects for preliminary development rather than fully fund them to fruition. This staged development yields early feedback which can improve an individual project through learning and a portfolio of projects by the termination of weaker ideas. Although prior empirical studies have found a positive relationship between staged development and outcomes, these studies were limited to settings that only allowed for learning, i.e. projects were never terminated. To capture the full effect, this paper exploits a form staged development in the television show industry where a new show’s first episode is filmed and evaluated before commissioning the full show. Using Netflix’s entry into television show production as a source of randomization, a negative treatment effect of piloting on show ratings is measured for shows that are more likely to survive piloting. Mechanism evidence suggests piloting worsens shows by impeding their ability to attract actors and lowering planning of the full season. Hence, staged development can have a downside: the lack of commitment to projects can prevent them from getting the resources and effort necessary for success.

International audience; Many software systems today provide support for adaptation and reconfiguration at runtime, in response to changes in their environment. Such adaptive systems are designed to run continuously and may not be shut down for reconfiguration or maintenance tasks. The variability of such systems has to be explicitly managed, together with mechanisms that control their runtime adaptation and reconfiguration. Dynamic software product lines (DSPLs) can help to achieve this. However, dealing with evolution is particularly challenging in a DSPL, as changes made at run-time can easily lead to inconsistencies. This paper describes the challenges of evolving DSPLs using an example cyber-physical system for home automation. We discuss the shortcomings of existing work and present a reference architecture to support DSPL evolution. To demonstrate its feasibility and flexibility, we implemented the proposed reference architecture for two different DSPLs: the aforementioned cyber-physical system, which uses feature models to describe its variability, and a runtime monitoring infrastructure, which is based on decision models. To assess the industrial applicability of our approach, we also implemented the reference architecture for a real-world DSPL, an automation software system for injection molding machines. Our results provide evidence on the flexibility, performance and industrial applicability of our approach.

Problem definition: Contrary to classic applications of matching theory, in most contemporary on-demand service platforms, matches can not be enforced because workers are flexible – they choose their tasks. Such flexibility makes it difficult to manage workers while keeping customers satisfied. We build a framework to compare platform matching policies with less flexible and more flexible workers, and empirically quantify by how much worker flexibility hurts customer satisfaction and customer equity. Academic/Practical relevance: In academic literature, there is no established framework that allows for the comparison of matching policies in on-demand platforms. Further, the link between worker flexibility and customer satisfaction is understudied. Methodology: We propose a tripartite framework for empirical evaluation and comparison of the operational policies with different degrees of worker flexibility. Step 1: Predictive modeling of customer satisfaction based on estimation of individual unobservable characteristics: customer difficulty and worker ability (item-response theory model). Step 2: Evaluation of the effect of matching policy (under a given level of flexibility) on customer satisfaction (bipartite matching). Step 3: Quantification of the associated monetary impact (customer lifetime value model). Results: We apply our framework to the dataset of one of the world's largest on-demand platforms for residential cleanings. We find that customer difficulty and cleaner ability are good predictors of customer satisfaction. Granting full flexibility to workers reduces customer satisfaction by 3% and customer lifetime revenue by 0.2%. We propose a family of matching policies that provide sufficient flexibility to workers, while alleviating 75% of the detrimental effect of worker flexibility on customer satisfaction. Managerial implications: Our results suggest that, in platforms with flexible workforce, the presence of worker and customer heterogeneity reduces customer satisfaction through matching inefficiency. Our empirical framework helps practitioners to decide on the right level of worker flexibility and the means for achieving it.

International audience; RPN (Robotic Programming Network) is an initiative to bring existing remote robot laboratories to a new dimension, by adding the flexibility and power of writing ROS code in an Internet browser and running it in the remote robot with a single click. The code is executed in the robot server at full speed, i.e. without any communication delay, and the output of the process is returned back. Built upon Robot Web Tools, RPN works out-of-the-box in any ROS-based robot or simulator. This paper presents the core functionality of RPN in the context of a web-enabled ROS system, its possibilities for remote education and training, and some experimentation with simulators and real robots in which we have integrated the tool in a Moodle environment, creating some programming courses and make it open to researchers and students (http: //robotprogramming.uji.es).