The Best of Two Worlds – Using Recent Advances from Uplift Modeling and Heterogeneous Treatment Effects to Optimize Targeting Policies

The design of targeting policies is fundamental to address a variety of practical problems across a broad spectrum of domains from e-commerce to politics and medicine. Recently, researchers and practitioners have begun to predict individual treatment effects to optimize targeting policies. Although different research streams, that is, uplift modeling and heterogeneous treatment effect propose numerous methods to predict individual treatment effects, current approaches suffer from various practical challenges, such as weak model performance and a lack of reliability. In this study, we propose a new, tree- based, algorithm that combines recent advances from both research streams and demonstrate how its use can improve predicting the individual treatment effect. We benchmark our method empirically against state-of-the-art strategies and show that the proposed algorithm achieves excellent results. We demonstrate that our approach performs particularly well when targeting few customers, which is of paramount interest when designing targeting policies in a marketing context

The Best of Two Worlds – Using Recent Advances from Uplift Modeling and Heterogeneous Treatment Effects to Optimize Targeting Policies

The design of targeting policies is fundamental to address a variety of practical problems across a broad spectrum of domains from e-commerce to politics and medicine. Recently, researchers and practitioners have begun to predict individual treatment effects to optimize targeting policies. Although different research streams, that is, uplift modeling and heterogeneous treatment effect propose numerous methods to predict individual treatment effects, current approaches suffer from various practical challenges, such as weak model performance and a lack of reliability. In this study, we propose a new, tree- based, algorithm that combines recent advances from both research streams and demonstrate how its use can improve predicting the individual treatment effect. We benchmark our method empirically against state-of-the-art strategies and show that the proposed algorithm achieves excellent results. We demonstrate that our approach performs particularly well when targeting few customers, which is of paramount interest when designing targeting policies in a marketing context

How Do Employees Perceive Digital Transformation and its Effects? A Theory of the Smart Machine Perspective

Digital transformation continues to profoundly impact employees in organizations in many industries. While past research has extensively investigated the impact of digital transformation on organizations, we still lack a comprehensive understanding of the factors that are relevant to examine how employees perceive digital transformation and its effects. One theory that explicitly has been built to account for a digital transformation in the past is the theory of the smart machine. To provide insights about relevant factors, we apply the theory of the smart machine and develop and evaluate an instrument to measure several of its key concepts for the first time. We build on established research guidelines to provide a 36-item survey instrument to measure the concepts Introduction of IT, Effects of automation, and Effects of informating. Finally, we provide implications for practice and further research

How Do Employees Perceive Digital Transformation and its Effects? A Theory of the Smart Machine Perspective

Digital transformation continues to profoundly impact employees in organizations in many industries. While past research has extensively investigated the impact of digital transformation on organizations, we still lack a comprehensive understanding of the factors that are relevant to examine how employees perceive digital transformation and its effects. One theory that explicitly has been built to account for a digital transformation in the past is the theory of the smart machine. To provide insights about relevant factors, we apply the theory of the smart machine and develop and evaluate an instrument to measure several of its key concepts for the first time. We build on established research guidelines to provide a 36-item survey instrument to measure the concepts Introduction of IT, Effects of automation, and Effects of informating. Finally, we provide implications for practice and further research

How Do Employees Perceive Digital Transformation and its Effects? A Theory of the Smart Machine Perspective

Digital transformation continues to profoundly impact employees in organizations in many industries. While past research has extensively investigated the impact of digital transformation on organizations, we still lack a comprehensive understanding of the factors that are relevant to examine how employees perceive the effects of digital transformation. One theory that explicitly has been built to account for a digital transformation in the past is the theory of the smart machine. To provide insights about relevant factors, we apply the theory of the smart machine and develop and evaluate an instrument to measure several of its key concepts for the first time. We build on established research guidelines to provide a 36-item survey instrument to measure the concepts Introduction of IT, Effects of automation, and Effects of informating. Finally, we provide implications for practice and further research

A novel fluorescent probe for NAD-consuming enzymes

A novel, fluorescent NAD derivative is processed as substrate by three different NAD-consuming enzymes. The new probe has been used to monitor enzymatic activity in a continuous format by changes in fluorescence and, in one case, to directly visualize alternative reaction pathways

Resistance Evolution to Bt Crops: Predispersal Mating of European Corn Borers

Over the past decade, the high-dose refuge (HDR) strategy, aimed at delaying the evolution of pest resistance to Bacillus thuringiensis (Bt) toxins produced by transgenic crops, became mandatory in the United States and is being discussed for Europe. However, precopulatory dispersal and the mating rate between resident and immigrant individuals, two features influencing the efficiency of this strategy, have seldom been quantified in pests targeted by these toxins. We combined mark-recapture and biogeochemical marking over three breeding seasons to quantify these features directly in natural populations of Ostrinia nubilalis, a major lepidopteran corn pest. At the local scale, resident females mated regardless of males having dispersed beforehand or not, as assumed in the HDR strategy. Accordingly, 0–67% of resident females mating before dispersal did so with resident males, this percentage depending on the local proportion of resident males (0% to 67.2%). However, resident males rarely mated with immigrant females (which mostly arrived mated), the fraction of females mating before dispersal was variable and sometimes substantial (4.8% to 56.8%), and there was no evidence for male premating dispersal being higher. Hence, O. nubilalis probably mates at a more restricted spatial scale than previously assumed, a feature that may decrease the efficiency of the HDR strategy under certain circumstances, depending for example on crop rotation practices