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We investigate the effects of low-carbon technology transfer between two rivalmanufacturers on their economic, environmental, and social welfare performance under a cap-and-trade policy. We model alternative licensing arrangements of technology transfer and evaluate the model performance from the perspectives of different stakeholders, including manufacturers, customers, and policy makers. Our findings show that the contractual choice on low-carbon technology licensing is dependent on the trade-off between the benefits gained from technology licensing and the consequential losses incurred from competition with a strengthened competitor, which is influenced by a combination of factors, including internal technological abilities, the interfirm power relationship, external market competition, and the carbon emission control policy. Among them, the interfirm power relationship is most influential in determining the optimal contractual decision. In addition, we extend the analysis of technology licensing strategies to different carbon emissions caps with additional cost incurred from purchasing emission allowances through auction, and a two-period model considering emissions cap reduction respectively. Finally, our analyses show it is critical for policy makers to develop appropriate emissions control policies to promote the agenda of a sustainable, low-carbon economy.

Objective The objective of this study is to understand the primary topics of consumer discussion on Twitter associated with telehealth for mental health or substance abuse for prepandemic versus during-pandemic time-periods, using a state-of-the-art machine learning (ML) natural language processing (NLP) method. Materials and Methods The primary methodological phases of this project were: (1) collecting, cleaning, and filtering data (tweets) from January 2014 to June 2021, (2) describing the final corpus, (3) running and optimizing Bidirectional Encoder Representations from Transformers (BERT; using BERTopic in Python) models, and (4) human refinement of topic model results and thematic classification of topics. Results The number of tweets in this context increased by 4 times during the pandemic (2017 tweets prepandemic vs 8672 tweets during the pandemic). During the pandemic topics were more frequently mental health related than substance abuse related. Top during-pandemic topics were therapy, suicide, pain (associated with burnout and drinking), and mental health diagnoses such as ADHD and autism. Anxiety was a key topic of discussion both pre- and during the pandemic. Discussion Telehealth for mental health and substance abuse is being discussed more frequently online, which implies growing demand. Given the topics extracted as proxies for demand, the most demand is currently for telehealth for mental health primarily, especially for children, parents, and therapy for those with anxiety or depression, and substance abuse secondarily. Conclusions Scarce telehealth resources can be allocated more efficiently if topics of consumer discussion are included in resource allocation decision- and policy-making processes.

Supply chain versus supply chain is an important aspect of business competition as the channel structure of each supply chain affects its own channel profit as well as the rival’s channel profit. Manufacturers have two distribution channel structure strategies: integrated channel and decentralized channel. The channel structure strategy can be influenced by price and service, two key competitive dimensions that affect both customer demand and firm profitability. The authors develop game-theoretic models to explore channel structure strategies for two manufacturers competing on price and service and examine how service provisions affect these strategies. Here, service can be provided by either producers (i.e., manufacturers) or sellers. When manufacturers provide service, this study reveals that symmetric decentralization (i.e., both manufacturers choosing a decentralized channel) is an equilibrium only when price elasticity, service cost, and service competition are low. When service is provided by sellers, symmetric decentralization is an equilibrium only when the retailer’s service cost is high and that of the manufacturer is low; otherwise, an asymmetric equilibrium may exist.

We have observed four modes of cost reduction in supply chains from practice. In particular, considering a supply chain with one manufacturer and one retailer, we identify four modes: the manufacturer solely invests, the retailer solely invests, or both firms collaboratively or independently invest to reduce the manufacturer's production cost. A question that arises naturally is which one is preferred by the manufacturer, the retailer, and the chain. With iso-elastic demand being sensitive to retail price and sales effort, we define channel powers (in addition to cost factors) for both firms to characterize the firms' investments and identify the best mode. We show that both firms are better off by investing collaboratively rather than independently if and only if the two firms have comparable channel powers and comparable cost factors. Moreover, a firm prefers to invest by itself solely rather than invest by its partner or both firms collaboratively if and only if its cost factor is sufficiently smaller than its partner's. We apply our results to supplier development and get some new insights. As a byproduct, the retailer may get less from the chain profit, which contradicts the retailer-dominant property in the literature under consignment contracts with revenue sharing. Finally, we show that our results are robust to different contract forms and whether the two firms have symmetric or asymmetric impacts on cost reduction.

BACKGROUND Prior US hospital telehealth (video visit) studies have focused on describing factors that influence telehealth adoption or performance effects for specific patient segments, hospital systems, or geographic regions. To our knowledge, a larger-scale, national-level (US) study has yet to be conducted on the causal impacts of hospital telehealth adoption as well as discontinuation. OBJECTIVE The aim of this study is to understand the causal impact of US hospital telehealth adoption or discontinuation on hospital performance from 2016 to 2018. METHODS We analyzed impacts of telehealth adoption or discontinuation by US hospitals on emergency department visits, total ambulatory visits (minus emergency department visits), outpatient services revenue, total facility expenses, and total hospital revenue for the 2016-2018 period. We specifically focused on performance effects for hospitals that switched from not having telehealth to adopting telehealth, or vice versa, during the 2016-2018 period, thus exploiting 2 quasi-natural experiments. We applied a difference-in-differences research design to each of the 2 main analyses. We compared hospitals that have made a telehealth change to groups of hospitals with similar characteristics that did not make a telehealth change, which established a counterfactual. To appropriately match hospitals between treatment and control groups, we applied propensity score matching. Our primary data were from the American Hospital Association Annual Survey and the Healthcare Cost Report Information System data. Several control variables were obtained from additional sources, including the Area Health Resource File and the Federal Communications Commission. RESULTS We found that telehealth adoption by US hospitals during the 2016-2018 period resulted in, on average, an increased number of total ambulatory visits (P=.008), increased total facility expenses (PP=.004) compared with the control group. We found that telehealth discontinuation during the same period resulted in, on average, decreased outpatient services revenue (P=.02) compared with the control group. CONCLUSIONS Our findings suggest that telehealth adoption increases use but has mixed impacts on performance, given that cost and revenue increase. However, once telehealth is offered, removing it can have a negative impact on performance, implying that returning to prior performance levels, if telehealth is removed, may be challenging.

We test the efficient market hypothesis by using machine learning to forecast future stock returns from historical performance. These forecasts strongly predict the cross section of future stock returns. The predictive power holds in most subperiods, is strong among the largest 500 stocks, and is distinct from momentum and reversal. The forecasting function has important nonlinearities and interactions and is remarkably stable through time. Our research design ensures that our findings are not a result of data mining. These findings question the efficient market hypothesis and indicate that investment strategies based on technical analysis and charting may have merit.

How successful are the SEC’s attempts to regulate dynamic risk in financial markets? Using mutual fund disclosure data from two financial shocks—the Puerto Rican debt crisis and COVID-19—we find evidence that SEC open-ended regulations, like the obligation to disclose changing market conditions, are largely successful in regulating dynamic, future risk. We find evidence of widespread and, often, detailed disclosures for new risks. But not all funds disclose new risks, and those that do vary in specificity ranging from individualized to generic disclosures. This creates perverse incentives for funds to opt-out of disclosure or downplay threats with boilerplate language when new risks are emerging. We recommend several SEC interventions to improve dynamic risk disclosures including empirically monitoring disclosures, issuing guidance when problematic variation is observed, and enforcing disclosure standards.

We propose a novel supervised dimension-reduction method called supervised t-distributed stochastic neighbor embedding (St-SNE) that achieves dimension reduction by preserving the similarities of data points in both feature and outcome spaces. The proposed method can be used for both prediction and visualization tasks with the ability to handle high-dimensional data. We show through a variety of data sets that when compared with a comprehensive list of existing methods, St-SNE has superior prediction performance in the ultrahigh-dimensional setting in which the number of features p exceeds the sample size n and has competitive performance in the p ≤ n setting. We also show that St-SNE is a competitive visualization tool that is capable of capturing within-cluster variations. In addition, we propose a penalized Kullback–Leibler divergence criterion to automatically select the reduced-dimension size k for St-SNE. Summary of Contribution: With the fast development of data collection and data processing technologies, high-dimensional data have now become ubiquitous. Examples of such data include those collected from environmental sensors, personal mobile devices, and wearable electronics. High-dimensionality poses great challenges for data analytics routines, both methodologically and computationally. Many machine learning algorithms may fail to work for ultrahigh-dimensional data, where the number of the features p is (much) larger than the sample size n. We propose a novel method for dimension reduction that can (i) aid the understanding of high-dimensional data through visualization and (ii) create a small set of good predictors, which is especially useful for prediction using ultrahigh-dimensional data.

Financial disclosures provide more information than simply their regulated content. Disclosure tone and textual attributes affect firm performance, earnings persistence, stock volatility, capital costs, and retail investor behavior. But do these theories apply to mutual fund disclosures? In this Article, we examine the extent to which disclosure tone predicts fund risk and performance. Following Loughran & McDonald (2011), we develop customized dictionaries specific to mutual funds. We then introduce a novel sentiment scoring framework that generates a transparent sentence- and disclosure-level score for our sample of 132,326 mutual fund summary prospectuses (497k) from 2010-2018. Descriptive analysis validates our dictionary by showing meaningful and statistically significant differences across disclosure sections, CRSP categories, and time. Principal risk (PR) sections are more negative (and uniformly so) than investment strategy (IS) sections across time. Using a fixed-effects model, we demonstrate sentiment's predictive power on fund's return, volatility, and portfolio composition, conditional on sentiment’s relationship with readability. Our context-sensitive approach provides researchers and regulators with a tool to better assess not only what fund disclosures are saying, but how they say it. Further, our results reveal persistent differences in PR and IS estimated information signals that map onto content regulations and suggest a rich field for future research.

Background Prior US hospital telehealth (video visit) studies have focused on describing factors that influence telehealth adoption or performance effects for specific patient segments, hospital systems, or geographic regions. To our knowledge, a larger-scale, national-level (US) study has yet to be conducted on the causal impacts of hospital telehealth adoption as well as discontinuation. Objective The aim of this study is to understand the causal impact of US hospital telehealth adoption or discontinuation on hospital performance from 2016 to 2018. Methods We analyzed impacts of telehealth adoption or discontinuation by US hospitals on emergency department visits, total ambulatory visits (minus emergency department visits), outpatient services revenue, total facility expenses, and total hospital revenue for the 2016-2018 period. We specifically focused on performance effects for hospitals that switched from not having telehealth to adopting telehealth, or vice versa, during the 2016-2018 period, thus exploiting 2 quasi-natural experiments. We applied a difference-in-differences research design to each of the 2 main analyses. We compared hospitals that have made a telehealth change to groups of hospitals with similar characteristics that did not make a telehealth change, which established a counterfactual. To appropriately match hospitals between treatment and control groups, we applied propensity score matching. Our primary data were from the American Hospital Association Annual Survey and the Healthcare Cost Report Information System data. Several control variables were obtained from additional sources, including the Area Health Resource File and the Federal Communications Commission. Results We found that telehealth adoption by US hospitals during the 2016-2018 period resulted in, on average, an increased number of total ambulatory visits (P=.008), increased total facility expenses (P Conclusions Our findings suggest that telehealth adoption increases use but has mixed impacts on performance, given that cost and revenue increase. However, once telehealth is offered, removing it can have a negative impact on performance, implying that returning to prior performance levels, if telehealth is removed, may be challenging.

This study examines the strategic choices of two rival manufacturers between competition and coopetition. A complex relationship exists between these two firms, who manufacture substitutive products. In addition, each of these firms has an option to purchase (or sell) a key component from (or to) the other. We develop a benchmark competition model and two coopetition models in which the manufacturers compete for end‐customer demand while simultaneously collaborating on component production through wholesaling (wholesaling coopetition) or licensing (license coopetition). By comparing the equilibria of the competition model and two coopetition models, we find that the optimal strategy for coopetition is determined by not only the degree of product substitution but also the inter–firm power relationship in the negotiation of a cooperation contract (i.e., wholesale price and license fees) and the difference in production efficiency between the two manufacturers. Our research comprehensively examines how the external, relationship‐specific, and internal factors affect firms’ optimal strategy selection and suggests a broad set of decision outcomes. Our study provides important managerial implications that can be utilized as strategic guidance for firms to pursue coopetition in various business environments.

We develop inventory control strategies for a supplier who is selling a product to a group of human newsvendors. Recent research in newsvendor experiments using human subjects revealed significant behavioral tendencies in the decision making processes of human newsvendors. If the supplier would incorporate these behavioral tendencies while managing her inventories, her costs would be significantly reduced. Using data from our own experiments, we estimate the possible reductions in supplier costs and determine the factors that significantly impact it. We observe a significant improvement in the supplier’s inventory decision if she estimates the demand distribution with a model that captures the anchoring tendencies instead of assuming aggregate randomness. As the model selection relies much on the data, it is an important task to determine whether the retailers are mean-anchoring, demand-chasing, both, or neither. In addition, we observe that the information about retailers’ behaviors is more beneficial for the supplier when the end-customer demands are more variable. It is certain that more precise information on the data will lead to a better inventory decision for suppliers. The information includes the retailers’ behavioral tendencies, individual-specific order behaviors, and the variability of end-customer demands.