Knowledge that Transforms

The recently enacted Inflation Reduction Act (IRA) includes a number of incentive-based programs (e.g., tax credits) designed to motivate firms to develop new clean technologies for fighting climate change. However, the IRA also includes a fee firms incur for excessive methane emissions. This represents the first time the United States government has ever levied a fee on greenhouse gas emissions, and it raises an interesting research question—how should a budget-constrained regulator balance the use of both incentive and penalty-based levers for stimulating investment in clean technology development? In this paper, we examine a regulator’s optimal penalty and subsidy decisions for motivating firms to invest in clean technology development. We illustrate how the level of competitive intensity in the market can influence a budget-constrained regulator with multiple competing objectives—the environment, firm profits, and consumer welfare. We find that a subsidy is always beneficial, irrespective of the regulator’s objective. While imposing a firm penalty always benefits the environment, it always negatively impacts the sum of firm profits and consumer welfare. However, depending on the level of competition in the market, instances can occur where imposing a high penalty actually benefits total firm profits or consumer welfare (separately). Interestingly, a regulator that cares about all three dimensions of its objective equally, should always set the penalty to either its minimum or maximum value, depending on whether the environmental cost of the harmful product is high or low.

With the rise of big data technology, an online platform can easily gather customer information to engage in price discrimination and obtain additional profits. Sharing customer information with a third-party seller increases the platform’s commission and information revenue, but the seller’s personalized pricing using customer information intensifies the price competition, which may damage the profitability of the platform’s own product. Whether to share information remains an unsolved strategy decision for the platform. We employ a game-theoretic model to characterize the interplay of information sharing by the platform and the pricing strategies of two firms. We consequently study four basic scenarios where the two firms adopt either uniform or personalized pricing policies. In equilibrium, the seller does not always have incentives to acquire information, and the platform is not always willing to share information. Intriguingly, with different combinations of the commission rate and the new consumer ratio, the equilibrium of the overall system has four possible results where the information may not be used for price discrimination. With a relatively high commission rate and a low new consumer ratio, the platform no longer pursues a demand for its own product and lets the seller occupy the whole market, which leads to the lowest consumer surplus and social welfare. We finally show that in the event of a relatively high commission rate, prohibiting information sharing increases consumer surplus and social welfare, verifying the necessity of regulations. These results could provide useful guidelines for platform managers and regulators to better design information sharing and price discrimination policies.

The fashion industry has consistently ranked high in terms of its association with modern slavery. While scandals like the Rana Plaza incident have focused the world's attention on Asia, in Brazil, over 35,000 people have been rescued from conditions analogous to slavery in the past 15 years. Outsourcing and the underlying social structures that blur the implementation of outsourcing are central to this problem. Fashion supply chains are highly fragmented and labour intensive, exhibiting power asymmetry and informality. Although supply chain research has focused on how focal firms can control or improve supplier practices, this study examines focal firm misconduct and decoupling. Our research presents an intervention aimed at developing a blockchain solution to create a census of fashion working conditions in Brazil. The project included two non-governmental organisations, two fashion brands and their suppliers, the fashion retail association, a blockchain start-up and the research team (18 organisations). We contribute to both theory and practice, revealing that the blockchain's potential to ease transaction costs is outweighed by governance costs related to third-party supervision. We show that focal firms justify unfair purchasing practices via victimisation and diverting attention from themselves to consumers (‘unwilling to pay’) and suppliers (‘not behaving as expected’), unveiling how these drivers of organisational misconduct lead to supplier decoupling (means–end) and focal firm decoupling (policy–practice). Such underlying social structures sustain inaction or, at best, advance focal firms’ visibility of their supply chains while offering no true transparency to the broader society.

Manufacturers and their resellers generally employ online intermediaries to sell their products. These intermediaries often possess superior demand information and decide whether to share it with their sellers. In practice, the manufacturers can employ two possible selling formats: reselling ([Formula: see text]), under which they sell the products to the intermediaries who subsequently resell to customers and agency selling ([Formula: see text]), under which they sell the products to customers through the intermediaries by paying a proportional fee. Additionally, the manufacturers can also sell the products to their resellers who subsequently resell to customers through the intermediaries. We develop a game-theoretic model to investigate an e-commerce supply chain in which an online intermediary first decides whether to disclose information option to a manufacturer and/or reseller; thereafter, the manufacturer selects between the [Formula: see text] or [Formula: see text] format. Regardless of the information-sharing decision made by the intermediary, the manufacturer’s selling format selection remains qualitatively unchanged. However, the intermediary can compel the manufacturer to change his selling format by sharing information with only the manufacturer or reseller in certain market environments. When the intensity is small but the proportional fee is intermediate, the intermediary should share information with only the manufacturer to compel him to change his selling format choice from [Formula: see text] to [Formula: see text]. When both the intensity and fee are intermediate, the intermediary should share information exclusively with the manufacturer (or reseller) to prompt a change in the manufacturer’s selling format, switching from [Formula: see text] to [Formula: see text] (or from [Formula: see text] to [Formula: see text]), contingent upon whether the magnitude of demand fluctuation is small or large.

The literature in sustainability has emphasized external pressures as a main driver of firms’ environmental responsibility. However, under similar external pressures, some firms search for environmental technology, while others do not. By focusing on information and communication technology (ICT) firms, we try to answer the question of when firms are motivated to search for renewable energy technology. Drawing on the framework of the behavioral theory of the firm, we propose that a mismatch between ICT firms’ CO 2 emissions performance and their aspiration to a certain level of emissions induce firms to evaluate their environmental performance status—whether they are aligned well with environmental requirements and their peer firms—and their technology status—whether they possess sufficient technologies to reduce emissions—which in turn affects their search for renewable energy technology. We corroborate our hypotheses using data on CO 2 emissions and renewable energy patents of U.S. ICT firms from 2010 to 2018. When we compare two groups of firms—one group whose emissions performance is poor and the other group whose emissions performance is good compared to their own past emissions—we find that the former is more likely to search for renewable energy technology compared to the latter. When we focus on each of the two groups, we find that firms decrease their search for renewable energy technology as the degree of poor emissions performance exacerbates (firms’ emissions increase above aspiration) or the degree of good emissions performance increases (firms’ emissions decrease below aspiration). Our findings have implications for public policy as well as firms’ environmentally sustainable operations.

Two empirical observations motivate the focus of this article. First, stemming from the increased demand for electronics, there is a significant change in the number of substitute product offerings. For example, within a product category, Eaton and Dell both offer remanufactured/refurbished products and new products to consumers. Second, to better manage expenses for e-waste, social planners levy fees on producers or consumers of electronic products. Integrating these two aspects, the key issue addressed is whether the social planner should levy a fee on the producer or consumer and whether such a fee should target one or both products. Through a rigorous analysis of the social planner’s fee decision, insights into differences between alternative policy choices are discussed. As expected, if the producer or consumer fee is levied on the same set of products, there are no differences in the impact on all stakeholders. Thus, the social planner would be indifferent in choosing whether to levy a fee on the producer or the consumer. On the other hand, analyzing current policies (e.g., Connecticut collects e-waste management fees from producers based on new product sales while California collects the fees from consumers purchasing new and refurbished/remanufactured products), we find significant differences for all stakeholders. Analytically, we show that pass-through fee effects of each policy are different. Through extensive numerical experiments, we find that the policy of a consumer fee on purchases of both products offers a greater chance of alignment between social planner and producer objectives; there are regions defined by parameters associated with remanufacturing activities where there exists a trade-off between the benefits to producers versus those for consumers; and there exist regions where the optimum policy for the social planner is aligned with the original equipment manufacturer’s preferred policy choice.

The sponsorship of sports or entertainment events is a long-established marketing promotion technique. The goals of sponsoring these events are to generate exposure and to transfer brand equity. A more assertive type of sponsored competition is when the competition is built around the products and brands of the sponsoring firm. The number of firms using sponsored product-based competitions (SPCs) is rising. Firms invest in these competitions to create content that highlights the capabilities of their products and build committed fan bases. However, evaluating the economic value of this type of sponsorship activity is challenging due to the absence of a direct link between event viewing and subsequent product consumption. We present an empirical approach to investigate the impacts of SPC on customer engagement using esports viewing and video game consumption data. We find that sponsored competitions provide firms with substantial benefits. Specifically, the results suggest that viewing an SPC event (i.e., esports competition) reduces consumers’ time between game plays by 3.2% (i.e., increased visit frequency), increases per-visit duration by 3.5%, and boosts the paid content purchase rate by 26%. These findings are corroborated using an alternate dataset from a second esports event from the game publisher. We find that the effect of an SPC event viewing is a nonlinear function of consumer expertise and is more pronounced among customers less familiar with the video game product category. In a supplementary analysis, we also identify an educational role of SPC that may explain the observed incremental consumption. The analyses reveal that esports audiences tend to choose frequently featured game characters with superior performance during the event. In addition, we find that when event viewers play the game with these characters, their performance improves. The results have substantial implications for event sponsorship management and follow-up product promotions.

“Cloud Kitchens” are delivery-only facilities that house multiple restaurants. Food-delivery platforms operate such kitchens to exploit two advantages: (a) Location advantage, arising due to a cloud-kitchen’s central location—this enables lower delivery times to customers. (b) Consolidation advantage, which accrues when multiple restaurants choose to co-locate at the cloud kitchen—this enables the platform to use a common pool of delivery drivers, thereby reducing costs. However, a cloud-kitchen’s eventual impacts on both the restaurants and the platform are intricately connected through their respective decisions—namely, the restaurants’ location decisions and the platform’s delivery capacity and delivery time. We examine conditions under which a cloud kitchen simultaneously benefits the primary stakeholders: delivery platform, restaurants, and customers. Our game-theoretic analysis considers two restaurants and a delivery platform. The restaurants simultaneously decide whether to stay at their initial (extreme) locations or relocate to a centrally located cloud kitchen. The platform decides the driver headcount and the delivery times for customers. In line with industry trends, we show that as population density increases beyond a threshold, the restaurants co-locating at the cloud kitchen is first a Pareto-dominant equilibrium and then the unique equilibrium. The platform and customers also prefer this equilibrium, leading to a win-win-win for the stakeholders. A cloud-kitchen’s benefit to the platform further increases as the drivers’ operational environment becomes more constrained, i.e. drivers’ carry-limit and speed decrease, and driver cost increases.

We document trends and patterns in the key attributes of the 24 top journals during 2001–2021 using data from the Web of Science. These trends include growth by disciplines and journals represented among selected journals in terms of growth in publications, relative publication share of disciplines, number of unique authors, attributes of prolific authors, and the extent of interdisciplinary contributions. We compare and contrast our findings with prior studies and offer a few conjectures on similarities and differences in the findings. We hope that our descriptive findings will spur further research on the production function of science and help identify the determinants of research productivity in top journals.

We consider an order fulfillment problem of an omni-channel retailer that ships online orders from its distribution center (DC) and brick-and-mortar stores. Stores use their local information, not observed by the retailer, that can lead them to accept or reject fulfillment requests of items in an online order. We investigate the problem of sequencing requests to stores and inventory rationing decisions at the DC to minimize expected costs under uncertain store acceptance behavior and when items are indistinguishable in terms of shipping. First, under the scenario that stores are used only when the DC has insufficient inventory, we propose a Markov Decision Process formulation and analyze the performance of myopic policies that are preferable because of their interpretability. We show that the performance rate of a myopic approach that orders stores by cost only depends on the number of items in an order, which is small in practice. We also determine conditions for the range of acceptance probabilities for the myopic policy to be optimal for small-sized orders. Using optimality conditions for a special case of the problem, we develop an adaptive variant of the myopic policy, and propose a new degree-based strategy that balances shipping costs and acceptance probabilities. Numerical testing suggests that the best-performing sequencing policy is within 1% of optimality on average. Moreover, using two years of data from a large omni-channel retailer in North America, we observe that adaptive policies, albeit more complex, are beneficial in reducing costs and split deliveries if acceptance rates can be estimated accurately. Second, we determine when the retailer should ship from stores or ration the inventory at the DC. We show that for single-item orders, the optimal policy has a threshold structure, where, remarkably, the highest priority region is also subject to rationing. We then consider the novel multi-unit-single-item rationing problem, and leverage the structure of the single-unit model to develop a heuristic. We numerically establish the efficacy of rationing models and our heuristic.