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Many firms are striving to improve their environmental positions by presenting their environmental efforts to the public. To do so, they are applying green marketing strategies to help gain competitive advantage and appeal to ecologically conscious consumers. However, not all green marketing claims accurately reflect firms' environmental conduct, and can be viewed as 'greenwashing'. Greenwashing may not only affect a company's profitability, but more importantly, result in ethical harm. Therefore, this research extends past greenwashing studies by examining additional influences on and outcomes of perceived greenwashing. To do so, we conducted two studies, an interview study with consumer product and consulting firms, as well as an experiment examining consumers interacting with a company website. For these studies, we used multiple methods, including interviews, questionnaires, and neurophysiological techniques. We found that perceived greenwashing relates not only to environmental and product perceptions, but also to consumers' happiness while interacting with the website. We also found that website interactivity relates to perceived greenwashing, environmental and product perceptions, and to the amount of interaction with the website. We conclude by discussing managerial and ethical implications for research and practice.

This study explores the nexus between digital and green transformations—the so-called “twin” transition—in European regions in an effort to identify the impact of digital and environmental technologies on the greenhouse gas (GHG) emissions originating from industrial production. We conduct an empirical analysis based on an original dataset that combines information on environmental and digital patent applications with information on GHG emissions from highly polluting plants for the period 2007–2016 at the metropolitan region level in the European Union and the UK. Results show that the local development of environmental technologies reduces GHG emissions, while the local development of digital technologies increases them, albeit in the latter case different technologies seem to have different impacts on the environment, with big data and computing infrastructures being the most detrimental. We also find differential impacts across regions depending on local endowment levels of the respective technologies: the beneficial effect of environmental technologies is stronger in regions with large digital technology endowments and, conversely, the detrimental effect of digital technologies is weaker in regions with large green technology endowments. Policy actions promoting the “twin” transition should take this evidence into account, in light of the potential downside of the digital transformation when not combined with the green transformation.

Predictive analytics is increasingly used to guide decision making in many applications. However, in practice, we often have limited data on the true predictive task of interest and must instead rely on more abundant data on a closely related proxy predictive task. For example, e-commerce platforms use abundant customer click data (proxy) to make product recommendations rather than the relatively sparse customer purchase data (true outcome of interest); alternatively, hospitals often rely on medical risk scores trained on a different patient population (proxy) rather than their own patient population (true cohort of interest) to assign interventions. Yet, not accounting for the bias in the proxy can lead to suboptimal decisions. Using real data sets, we find that this bias can often be captured by a sparse function of the features. Thus, we propose a novel two-step estimator that uses techniques from high-dimensional statistics to efficiently combine a large amount of proxy data and a small amount of true data. We prove upper bounds on the error of our proposed estimator and lower bounds on several heuristics used by data scientists; in particular, our proposed estimator can achieve the same accuracy with exponentially less true data (in the number of features d ). Finally, we demonstrate the effectiveness of our approach on e-commerce and healthcare data sets; in both cases, we achieve significantly better predictive accuracy as well as managerial insights into the nature of the bias in the proxy data. This paper was accepted by George Shanthikumar, big data and analytics.

Gross domestic product (GDP) and derived metrics such as productivity have been central to our understanding of economic progress and well-being. In principle, changes in consumer surplus provide a superior, and more direct, measure of changes in well-being, especially for digital goods. In practice, these alternatives have been difficult to quantify. We explore the potential of massive online choice experiments to measure consumer surplus. We illustrate this technique via several empirical examples which quantify the valuations of popular digital goods and categories. Our examples include incentive-compatible discrete-choice experiments where online and laboratory participants receive monetary compensation if and only if they forgo goods for predefined periods. For example, the median user needed a compensation of about $48 to forgo Facebook for 1 mo. Our overall analyses reveal that digital goods have created large gains in well-being that are not reflected in conventional measures of GDP and productivity. By periodically querying a large, representative sample of goods and services, including those which are not priced in existing markets, changes in consumer surplus and other new measures of well-being derived from these online choice experiments have the potential for providing cost-effective supplements to the existing national income and product accounts.

This article argues that firms’ balance sheets were instrumental in the transmission of consumer demand shocks during the Great Recession. Using micro-level data from the U.S. Census Bureau, we find that establishments of more highly levered firms experienced significantly larger employment losses in response to declines in local consumer demand. These results are not driven by firms being less productive, having expanded too much prior to the Great Recession, or being generally more sensitive to fluctuations in either aggregate employment or house prices. Likewise, at the county level, we find that counties with more highly levered firms experienced significantly larger declines in employment in response to local consumer demand shocks. Accordingly, firms’ balance sheets also matter for aggregate employment. Our results suggest a possible role for employment policies that target firms directly besides conventional stimulus.

Firms collect an increasing amount of consumer feedback in the form of unstructured consumer reviews. These reviews contain text about consumer experiences with products and services that are different from surveys that query consumers for specific information. A challenge in analyzing unstructured consumer reviews is in making sense of the topics that are expressed in the words used to describe these experiences. We propose a new model for text analysis that makes use of the sentence structure contained in the reviews and show that it leads to improved inference and prediction of consumer ratings relative to existing models using data from www.expedia.com and www.we8there.com . Sentence-based topics are found to be more distinguished and coherent than those identified from a word-based analysis. Data, as supplemental material, are available at https://doi.org/10.1287/mksc.2016.0993 .

In hopes of motivating consumers to provide larger volumes of useful reviews, many retailers offer financial incentives. Here, we explore an alternative approach, social norms. We inform individuals about the volume of reviews authored by peers. We test the effectiveness of using financial incentives, social norms, and a combination of both strategies in motivating consumers. In two randomized experiments, one in the field conducted in partnership with a large online clothing retailer based in China and a second on Amazon Mechanical Turk, we compare the effectiveness of each strategy in stimulating online reviews in larger numbers and of greater length. We find that financial incentives are more effective at inducing larger volumes of reviews, but the reviews that result are not particularly lengthy, whereas social norms have a greater effect on the length of reviews. Importantly, we show that the combination of financial incentives and social norms yields the greatest overall benefit by motivating reviews in greater numbers and of greater length. We further assess treatment-induced self-selection and sentiment bias by triangulating the experimental results with findings from an observational study. The online appendix is available at https://doi.org/10.1287/mnsc.2016.2715 . This paper was accepted by Chris Forman, information systems.

We examine the health plan choices that 23,894 employees at a U.S. firm made from a large menu of options that differed only in financial cost-sharing and premium. These decisions provide a clear test of the predictions of the standard economic model of insurance choice in the absence of choice frictions because plans were priced so that nearly every plan with a lower deductible was financially dominated by an otherwise identical plan with a high deductible. We document that the majority of employees chose dominated plans, which resulted in excess spending equivalent to 24% of chosen plan premiums. Low-income employees were significantly more likely to choose dominated plans, and most employees did not switch into more financially efficient plans in the subsequent year. We show that the choice of dominated plans cannot be rationalized by standard risk preference or any expectations about health risk. Testing alternative explanations with a series of hypothetical-choice experiments, we find that the popularity of dominated plans was not primarily driven by the size and complexity of the plan menu, nor informed preferences for avoiding high deductibles, but by employees’ lack of understanding of health insurance. Our findings challenge the standard practice of inferring risk preferences from insurance choices and raise doubts about the welfare benefits of health reforms that expand consumer choice.

Measuring consumer responsiveness to medical care prices is a central issue in health economics and a key ingredient in the optimal design and regulation of health insurance markets. We leverage a natural experiment at a large self-insured firm that required all of its employees to switch from an insurance plan that provided free health care to a nonlinear, high-deductible plan. The switch caused a spending reduction between 11.8% and 13.8% of total firm-wide health spending. We decompose this spending reduction into the components of (i) consumer price shopping, (ii) quantity reductions, and (iii) quantity substitutions and find that spending reductions are entirely due to outright reductions in quantity. We find no evidence of consumers learning to price shop after two years in high-deductible coverage. Consumers reduce quantities across the spectrum of health care services, including potentially valuable care (e.g., preventive services) and potentially wasteful care (e.g., imaging services). To better understand these changes, we study how consumers respond to the complex structure of the highdeductible contract. Consumers respond heavily to spot prices at the time of care, reducing their spending by 42% when under the deductible, conditional on their true expected end-of-year price and their prior year end-of-year marginal price. There is no evidence of learning to respond to the true shadow price in the second year post-switch.

We conduct an artefactual field experiment using a diversified sample of passengers of public transportation to study attitudes toward dishonesty. We find that the diversity of behavior in terms of (dis)honesty in laboratory tasks and in the field correlate. Moreover, individuals who have just been fined in the field behave more honestly in the lab than the other fare dodgers, except when context is introduced. Overall, we show that simple tests of dishonesty in the lab can predict moral firmness in life, although fraudsters who care about social image cheat less when behavior can be verified ex post by the experimenter. Data and the online appendix are available at https://doi.org/10.1287/mnsc.2016.2616 . This paper was accepted by Uri Gneezy, behavioral economics .