Journal of Industry, Competition and Trade - In this paper, we analyze the collaboration between an environmental group (EG) and polluting firms when they are asymmetric in their abatement costs.... 相似文献
Experimental research methods have a long history across a number of different disciplines—including consumer research. Although experiments are just one of many alternative research methods, experiments are notable because they are the best way to establish causation. This makes experiments a powerful tool when researchers need to show cause and effect relationships. In this article, we provide best practices for implementing experimental research methods in consumer studies. Specifically, we discuss several important topics researchers need to consider when designing experiments, including developing hypotheses, operationalizing the variables (manipulated or measured), deciding on the research design (between-subjects, within-subjects, or mixed), selecting the research setting (laboratory, field, or online), understanding the main effect (via moderation, mediation, or moderated mediation), including manipulation and attention checks, determining the sample size, and choosing participants. We provide recommendations that researchers can use to conduct high-quality experiments in a consumer context. 相似文献
Machine Learning (ML) excels at most predictive tasks but its complex nonparametric structure renders it less useful for inference and out-of sample predictions. This article aims to elucidate and enhance the analytical capabilities of ML in real estate through Interpretable ML (IML). Specifically, we compare a hedonic ML approach to a set of model-agnostic interpretation methods. Our results suggest that IML methods permit a peek into the black box of algorithmic decision making by showing the web of associative relationships between variables in greater resolution. In our empirical applications, we confirm that size and age are the most important rent drivers. Further analysis reveals that certain bundles of hedonic characteristics, such as large apartments in historic buildings with balconies located in affluent neighborhoods, attract higher rents than adding up the contributions of each hedonic characteristic. Building age is shown to exhibit a U-shaped pattern in that both the youngest and oldest buildings attract the highest rents. Besides revealing valuable distance decay functions for spatial variables, IML methods are also able to visualise how the strength and interactions of hedonic characteristics change over time, which investors could use to determine the types of assets that perform best at any given stage of the real estate investment cycle. 相似文献
Health improved in English cities in the last third of the nineteenth century, in tandem with substantial increases in public spending on water supplies and sanitation. However, previous efforts to measure the contribution of public expenditures to mortality improvements have been hampered by difficulties in quantifying public health investments and the lack of mortality data for specifically urban populations. We improve upon the existing evidence base by (1) creating measures of the stock of urban district sanitary capital, by type, on the basis of capital expenditure flows, rather than loan stocks; (2) using mortality and capital stock data that relate to the same administrative units (urban districts), and (3) studying the period 1880–1909 as well as the earlier period from 1845. The stock of sewerage capital was robustly related to improvements in all-cause mortality after 1880. The size of this effect varied with the extent of public investment in water supplies, suggesting complementarity between the two assets. For the period 1845–84, investments in water were associated with declines in infant and child mortality but the effect was much smaller and less precisely estimated in later decades. Our results suggest that improvements in water and sewerage targeted different transmission pathways for faecal–oral diseases. 相似文献
Autonomous cars are considered to be the next disruptive innovation that will affect consumers. It can be expected that not only traditional automakers will enter this market (e.g., Ford) but also technology companies (e.g., Google) and newer companies dedicated to self-driving cars (e.g., Tesla). We take a brand extension perspective and analyze to what extent consumers prefer autonomous cars from these brand categories. Our empirical study is based on discrete choice experiments about adopting autonomous vehicles in a purchase scenario and in a renting context. Our findings show that brands play a central role when making autonomous driving decisions. Brand preferences differ systematically when buying versus renting a self-driving car. While technology brands are most preferred overall, consumers favor automaker brands over new brands only when purchasing, not when renting. We further disentangle the brand strength into the marginal effects of image associations. For example, Google’s strong brand positioning can be explained by experiences with the parent brand, but it could still improve brand strength by highlighting the relevance of the associated brand portfolio for self-driving cars. The effect of these brand extension success factors differs between parent-brand categories and also between the renting and purchasing scenarios, which requires a dedicated brand management.