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10,506 result(s) for "Defection"
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Punishment diminishes the benefits of network reciprocity in social dilemma experiments
Network reciprocity has been widely advertised in theoretical studies as one of the basic cooperation-promoting mechanisms, but experimental evidence favoring this type of reciprocity was published only recently. When organized in an unchanging network of social contacts, human subjects cooperate provided the following strict condition is satisfied: The benefit of cooperation must outweigh the total cost of cooperating with all neighbors. In an attempt to relax this condition, we perform social dilemma experiments wherein network reciprocity is aided with another theoretically hypothesized cooperation-promoting mechanism—costly punishment. The results reveal how networks promote and stabilize cooperation. This stabilizing effect is stronger in a smaller-size neighborhood, as expected from theory and experiments. Contrary to expectations, punishment diminishes the benefits of network reciprocity by lowering assortment, payoff per round, and award for cooperative behavior. This diminishing effect is stronger in a larger-size neighborhood. An immediate implication is that the psychological effects of enduring punishment override the rational response anticipated in quantitative models of cooperation in networks.
Solar cell surface defect inspection based on multispectral convolutional neural network
Similar and indeterminate defect detection of solar cell surface with heterogeneous texture and complex background is a challenge of solar cell manufacturing. The traditional manufacturing process relies on human eye detection which requires a large number of workers without a stable and good detection effect. In order to solve the problem, a visual defect detection method based on multi-spectral deep convolutional neural network (CNN) is designed in this paper. Firstly, a selected CNN model is established. By adjusting the depth and width of the model, the influence of model depth and kernel size on the recognition result is evaluated. The optimal CNN model structure is selected. Secondly, the light spectrum features of solar cell color image are analyzed. It is found that a variety of defects exhibited different distinguishable characteristics in different spectral bands. Thus, a multi-spectral CNN model is constructed to enhance the discrimination ability of the model to distinguish between complex texture background features and defect features. Finally, some experimental results and K-fold cross validation show that the multi-spectral deep CNN model can effectively detect the solar cell surface defects with higher accuracy and greater adaptability. The accuracy of defect recognition reaches 94.30%. Applying such an algorithm can increase the efficiency of solar cell manufacturing and make the manufacturing process smarter.
True Believers, Deserters, and Traitors: Who Leaves Insurgent Groups and Why
Anti-insurgent militias and states attempt to erode insurgent groups' capacities and co-opt insurgent fighters by promising and providing benefits. They do so to create a perception that the insurgency is unraveling and to harness inside information to prosecute more effective counterinsurgency campaigns. Why do some insurgents defect to a paramilitary group and others exit the war by demobilizing, while still others remain loyal to their group? This article presents the first empirical analysis of these questions, connecting insurgents' motivations for joining, wartime experiences, and organizational behavior with decisions to defect. A survey of ex-combatants in Colombia shows that individuals who joined for ideological reasons are less likely to defect overall but more likely to side-switch or demobilize when their group deviates from its ideological precepts. Among fighters who joined for economic reasons, political indoctrination works to decrease their chances of demobilization and defection to paramilitaries, while opportunities for looting decrease economically motivated combatants' odds of defection.
The Value of Self-Service
Advancements in information technology have changed the way customers experience a service encounter and their relationship with service providers. Especially technology-based self-service channels have found their way into the 21st century service economy. While research embraces these channels for their cost-efficiency, it has not examined whether a shift from personal to self-service affects customer–firm relationships. Drawing from the service-dominant logic and its central concept of value-in-context, we discuss customers’ value creation in self-service and personal service channels and examine the long-term impact of these channels on customer retention. Using longitudinal customer data, we investigate how the ratio of self-service versus personal service use influences customer defection over time. Our findings suggest that the ratio of self-service to personal service used affects customer defection in a U-shaped manner, with intermediate levels of both self-service and personal service use being associated with the lowest likelihood of defection. We also find that this effect mitigates over time. We conclude that firms should not shift customers toward self-service channels completely, especially not at the beginning of a relationship. Our study underlines the importance of understanding when and how self-service technologies create valuable customer experiences and stresses the notion of actively managing customers’ cocreation of value.